2026 |
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 | FRESHR-GSI: A Generalized Safety Model and Evaluation Framework for Mobile Robots in Multi-Human Environments Conference Forthcoming 2026 IEEE International Conference on Robotics & Automation (ICRA), Forthcoming. Abstract | Links | BibTeX | Tags: control, evaluation, human-robot interaction @conference{Pandey2026, title = {FRESHR-GSI: A Generalized Safety Model and Evaluation Framework for Mobile Robots in Multi-Human Environments}, author = {Pranav Pandey and Ramviyas Parasuraman and Prashant Doshi}, url = {https://arxiv.org/abs/2501.03467}, year = {2026}, date = {2026-06-01}, booktitle = {2026 IEEE International Conference on Robotics & Automation (ICRA)}, abstract = {Human safety is critical in applications involving close human-robot interactions (HRI) and is a key aspect of physical compatibility between humans and robots. While measures of human safety in HRI exist, these mainly target industrial settings involving robotic manipulators. Less attention has been paid to settings where mobile robots and humans share the space. This paper introduces a new robot-centered directional framework of human safety. It is particularly useful for evaluating mobile robots as they operate in environments populated by multiple humans. The framework integrates several key metrics, such as each human’s relative distance, speed, and orientation. The core novelty lies in the framework’s flexibility to accommodate different application requirements while allowing for both the robot-centered and external observer points of view. We instantiate the framework by using RGB-D based vision integrated with a deep learning-based human detection pipeline to yield a proxemics-guided generalized safety index (GSI) that instantaneously assesses human safety. We extensively validate GSI’s capability of producing appropriate and fine-grained safety measures in real-world experimental scenarios and demonstrate its superior efficacy against extant safety models.}, keywords = {control, evaluation, human-robot interaction}, pubstate = {forthcoming}, tppubtype = {conference} } Human safety is critical in applications involving close human-robot interactions (HRI) and is a key aspect of physical compatibility between humans and robots. While measures of human safety in HRI exist, these mainly target industrial settings involving robotic manipulators. Less attention has been paid to settings where mobile robots and humans share the space. This paper introduces a new robot-centered directional framework of human safety. It is particularly useful for evaluating mobile robots as they operate in environments populated by multiple humans. The framework integrates several key metrics, such as each human’s relative distance, speed, and orientation. The core novelty lies in the framework’s flexibility to accommodate different application requirements while allowing for both the robot-centered and external observer points of view. We instantiate the framework by using RGB-D based vision integrated with a deep learning-based human detection pipeline to yield a proxemics-guided generalized safety index (GSI) that instantaneously assesses human safety. We extensively validate GSI’s capability of producing appropriate and fine-grained safety measures in real-world experimental scenarios and demonstrate its superior efficacy against extant safety models. |
 | DCL-Sparse: Distributed Relative Localization in Sparse Graphs Conference Forthcoming 2026 IEEE International Conference on Robotics & Automation (ICRA), Forthcoming. Abstract | Links | BibTeX | Tags: localization, multi-robot systems, networking @conference{Sagale2026, title = {DCL-Sparse: Distributed Relative Localization in Sparse Graphs}, author = {Atharva Sagale and Tohid Kargar Tasooji and Ramviyas Parasuraman}, url = {https://arxiv.org/abs/2412.14793}, year = {2026}, date = {2026-06-01}, booktitle = {2026 IEEE International Conference on Robotics & Automation (ICRA)}, abstract = {This paper presents a novel approach to range-based cooperative localization for robot swarms in GPS-denied environments, addressing the limitations of current methods in noisy and sparse settings. We propose a robust multi-layered localization framework that combines shadow edge localization techniques with the strategic deployment of UAVs. This approach not only addresses the challenges associated with nonrigid and poorly connected graphs but also enhances the convergence rate of the localization process. We introduce two key concepts: the S1-Edge approach in our distributed protocol to address the rigidity problem of sparse graphs and the concept of a powerful UAV node to increase the sensing and localization capability of the multi-robot system. Our approach leverages the advantages of the distributed localization methods, enhancing scalability and adaptability in large robot networks. We establish theoretical conditions for the new S1-Edge that ensure solutions exist even in the presence of noise, thereby validating the effectiveness of shadow edge localization. Extensive simulation experiments confirm the superior performance of our method compared to state-of-the-art techniques, resulting in up to 95% reduction in localization error, demonstrating substantial improvements in localization accuracy and robustness to sparse graphs. This work provides a decisive advancement in the field of multi-robot localization, offering a powerful tool for high-performance and reliable operations in challenging environments. }, keywords = {localization, multi-robot systems, networking}, pubstate = {forthcoming}, tppubtype = {conference} } This paper presents a novel approach to range-based cooperative localization for robot swarms in GPS-denied environments, addressing the limitations of current methods in noisy and sparse settings. We propose a robust multi-layered localization framework that combines shadow edge localization techniques with the strategic deployment of UAVs. This approach not only addresses the challenges associated with nonrigid and poorly connected graphs but also enhances the convergence rate of the localization process. We introduce two key concepts: the S1-Edge approach in our distributed protocol to address the rigidity problem of sparse graphs and the concept of a powerful UAV node to increase the sensing and localization capability of the multi-robot system. Our approach leverages the advantages of the distributed localization methods, enhancing scalability and adaptability in large robot networks. We establish theoretical conditions for the new S1-Edge that ensure solutions exist even in the presence of noise, thereby validating the effectiveness of shadow edge localization. Extensive simulation experiments confirm the superior performance of our method compared to state-of-the-art techniques, resulting in up to 95% reduction in localization error, demonstrating substantial improvements in localization accuracy and robustness to sparse graphs. This work provides a decisive advancement in the field of multi-robot localization, offering a powerful tool for high-performance and reliable operations in challenging environments. |
 | Multi-Robot Informative Sampling and Coverage in GPS-Denied Environments Conference Forthcoming 2026 IEEE International Conference on Robotics & Automation (ICRA), Forthcoming. Abstract | BibTeX | Tags: control, cooperation, localization, mapping, multi-robot systems, planning @conference{Munir2026, title = {Multi-Robot Informative Sampling and Coverage in GPS-Denied Environments}, author = {Aiman Munir and Ehsan Latif and Ramviyas Parasuraman}, year = {2026}, date = {2026-06-01}, booktitle = {2026 IEEE International Conference on Robotics & Automation (ICRA)}, abstract = {Multi-Robot Systems (MRS) in GPS-denied environments such as indoor spaces, subterranean areas, and urban canyons face the dual challenge of localizing themselves while performing informative path planning (IPP) to model unknown spatial fields. Current IPP methods rely heavily on GPS for localization, limiting their applicability in GPS-denied settings, while existing approaches addressing observation uncertainty fail to account for localization uncertainty that degrades mapping accuracy. This paper presents Anchor-Oriented IPP (AO-IPP), a framework that coordinates robot teams through relative positioning using Access Points and uncertainty-driven transitions between three phases: anchor point localization, informative sampling for field estimation, and spatial coverage optimization. Each robot maintains dual Gaussian Process models with transitions driven by uncertainty levels rather than fixed time schedules. Extensive simulations and real-world experiments demonstrate that AO-IPP achieves performance comparable to GPS-based IPP algorithms while outperforming existing methods in balancing IPP and coverage objectives by up to 54%. The approach exhibits sublinear regret bounds and enables autonomous coordination in challenging environments previously inaccessible to traditional IPP methods, providing a robust solution for environmental monitoring, exploration, and mapping applications requiring both accurate field estimation and comprehensive spatial coverage.}, keywords = {control, cooperation, localization, mapping, multi-robot systems, planning}, pubstate = {forthcoming}, tppubtype = {conference} } Multi-Robot Systems (MRS) in GPS-denied environments such as indoor spaces, subterranean areas, and urban canyons face the dual challenge of localizing themselves while performing informative path planning (IPP) to model unknown spatial fields. Current IPP methods rely heavily on GPS for localization, limiting their applicability in GPS-denied settings, while existing approaches addressing observation uncertainty fail to account for localization uncertainty that degrades mapping accuracy. This paper presents Anchor-Oriented IPP (AO-IPP), a framework that coordinates robot teams through relative positioning using Access Points and uncertainty-driven transitions between three phases: anchor point localization, informative sampling for field estimation, and spatial coverage optimization. Each robot maintains dual Gaussian Process models with transitions driven by uncertainty levels rather than fixed time schedules. Extensive simulations and real-world experiments demonstrate that AO-IPP achieves performance comparable to GPS-based IPP algorithms while outperforming existing methods in balancing IPP and coverage objectives by up to 54%. The approach exhibits sublinear regret bounds and enables autonomous coordination in challenging environments previously inaccessible to traditional IPP methods, providing a robust solution for environmental monitoring, exploration, and mapping applications requiring both accurate field estimation and comprehensive spatial coverage. |
 | Imitation-BT: Automating Behavior Tree Generation by Echoing Reinforcement Learning Agents Conference Forthcoming 2026 IEEE International Conference on Robotics & Automation (ICRA), Forthcoming. Abstract | BibTeX | Tags: autonomy, behavior-trees, learning, planning @conference{Bthula2026, title = {Imitation-BT: Automating Behavior Tree Generation by Echoing Reinforcement Learning Agents}, author = {Shailendra Sekhar Bthula and Ramviyas Parasuraman}, year = {2026}, date = {2026-06-01}, booktitle = {2026 IEEE International Conference on Robotics & Automation (ICRA)}, abstract = {Understanding an autonomous agent's decision-making prowess is of paramount importance, as it increases trust and guarantees safety. Although agent policies learned through reinforcement learning (RL) and machine learning (ML) paradigms have demonstrated their dominance in various domains, they struggle with deployment in high-stakes environments due to their algorithmic opacity. A structured and transparent representation of a policy helps us understand, evaluate, and modify it if necessary. Due to their inherent reactivity, modularity, and transparent hierarchical representation, the Behavior Tree (BT) is an ideal solution to represent control policies. In this paper, we focus on building a knowledge representation transfer framework in which knowledge of trained RL agents is captured through imitation learning and then utilized to form a compact BT. Our primary focus is to retain maximum performance while improving the interpretability of the BTs. In combination with planning and learning, we automate the formation of a BT and offer an alternative, transparent architecture for policy representation. In an extensive analysis with a variety of gymnasium environments and the Robotics Package Delivery domain simulations, we demonstrate the significant performance retention capability and superior interpretability of the proposed Imitation-BT. }, keywords = {autonomy, behavior-trees, learning, planning}, pubstate = {forthcoming}, tppubtype = {conference} } Understanding an autonomous agent's decision-making prowess is of paramount importance, as it increases trust and guarantees safety. Although agent policies learned through reinforcement learning (RL) and machine learning (ML) paradigms have demonstrated their dominance in various domains, they struggle with deployment in high-stakes environments due to their algorithmic opacity. A structured and transparent representation of a policy helps us understand, evaluate, and modify it if necessary. Due to their inherent reactivity, modularity, and transparent hierarchical representation, the Behavior Tree (BT) is an ideal solution to represent control policies. In this paper, we focus on building a knowledge representation transfer framework in which knowledge of trained RL agents is captured through imitation learning and then utilized to form a compact BT. Our primary focus is to retain maximum performance while improving the interpretability of the BTs. In combination with planning and learning, we automate the formation of a BT and offer an alternative, transparent architecture for policy representation. In an extensive analysis with a variety of gymnasium environments and the Robotics Package Delivery domain simulations, we demonstrate the significant performance retention capability and superior interpretability of the proposed Imitation-BT. |
 | Energy-Aware Informative Path Planning for Heterogeneous Multi-Robot Systems Conference Forthcoming 2026 IEEE International Conference on Robotics & Automation (ICRA), Forthcoming. Abstract | BibTeX | Tags: control, cooperation, multi-robot systems, planning @conference{Munir2026b, title = {Energy-Aware Informative Path Planning for Heterogeneous Multi-Robot Systems}, author = {Aiman Munir and Aryan Dutta and Ramviyas Parasuraman}, year = {2026}, date = {2026-06-01}, booktitle = {2026 IEEE International Conference on Robotics & Automation (ICRA)}, abstract = {Effective energy management is essential for maximizing information gathering tasks with networked mobile robots, particularly for large-scale, energy-intensive tasks such as agricultural monitoring and wildfire mapping. This paper presents a novel framework that integrates robots’ energy profiles with confidence bounds of their assigned regions to optimize sampling targets. Designed for persistent, long-term deployments, the framework employs Gaussian Process Regression (GPR) to maximize data acquisition and accurately reconstruct unknown spatial distributions (e.g., algae outbreaks or humidity maps). The method enables seamless transitions between exploration (mapping uncertain regions when energy is high), exploitation (refining maps at moderate energy levels), and recharging (navigating to charging stations when energy is low), to achieve energy-balanced informative path planning. Experiments demonstrate the effectiveness of the approach against state-of-the-art methods in generating energy-efficient and distinct paths for heterogeneous robots, delivering up to 32% energy savings while maintaining high reconstruction accuracy. Hardware experiments closely matched the performance in simulation.}, keywords = {control, cooperation, multi-robot systems, planning}, pubstate = {forthcoming}, tppubtype = {conference} } Effective energy management is essential for maximizing information gathering tasks with networked mobile robots, particularly for large-scale, energy-intensive tasks such as agricultural monitoring and wildfire mapping. This paper presents a novel framework that integrates robots’ energy profiles with confidence bounds of their assigned regions to optimize sampling targets. Designed for persistent, long-term deployments, the framework employs Gaussian Process Regression (GPR) to maximize data acquisition and accurately reconstruct unknown spatial distributions (e.g., algae outbreaks or humidity maps). The method enables seamless transitions between exploration (mapping uncertain regions when energy is high), exploitation (refining maps at moderate energy levels), and recharging (navigating to charging stations when energy is low), to achieve energy-balanced informative path planning. Experiments demonstrate the effectiveness of the approach against state-of-the-art methods in generating energy-efficient and distinct paths for heterogeneous robots, delivering up to 32% energy savings while maintaining high reconstruction accuracy. Hardware experiments closely matched the performance in simulation. |
2025 |
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 | SPACE: 3D Spatial Co-operation and Exploration Framework for Robust Mapping and Coverage with Multi-Robot Systems Journal Article IEEE Robotics and Automation Letters, 10 (12), pp. 13074–13081, 2025. Abstract | Links | BibTeX | Tags: cooperation, multi-robot systems, perception @article{Ghanta2025, title = {SPACE: 3D Spatial Co-operation and Exploration Framework for Robust Mapping and Coverage with Multi-Robot Systems}, author = {Sai Krishna Ghanta and Ramviyas Parasuraman}, url = {https://ieeexplore.ieee.org/document/11222879}, doi = {10.1109/LRA. 2025.3627118}, year = {2025}, date = {2025-12-12}, journal = {IEEE Robotics and Automation Letters}, volume = {10}, number = {12}, pages = {13074–13081}, abstract = {In indoor environments, multi-robot visual (RGB-D) mapping and exploration hold immense potential for application in domains such as domestic service and logistics, where deploying multiple robots in the same environment can significantly enhance efficiency. However, there are two primary challenges: (1) the “ghosting trail” effect, which occurs when inter-robot views overlap, producing temporally inconsistent, duplicated surfaces that degrade point-cloud reconstruction accuracy, and (2) the oversight of visual reconstructions in selecting the most effective frontiers for exploration. Given these challenges are interrelated, we address them together by proposing a new semi-distributed framework (SPACE) for spatial cooperation in indoor environments that enables enhanced coverage and 3D mapping. SPACE leverages geometric techniques, including “mutual awareness” and a “dynamic robot filter,” to overcome spatial mapping constraints. Additionally, we introduce a novel spatial frontier detection system and map merger, integrated with an adaptive frontier assigner for optimal coverage balancing the exploration and reconstruction objectives. In extensive ROS-Gazebo simulations and real-world experiments, SPACE demonstrated superior performance over state-of-the-art approaches in both exploration and mapping metrics, demonstrating significant mitigation of the ghosting effects by multiple magnitudes.}, keywords = {cooperation, multi-robot systems, perception}, pubstate = {published}, tppubtype = {article} } In indoor environments, multi-robot visual (RGB-D) mapping and exploration hold immense potential for application in domains such as domestic service and logistics, where deploying multiple robots in the same environment can significantly enhance efficiency. However, there are two primary challenges: (1) the “ghosting trail” effect, which occurs when inter-robot views overlap, producing temporally inconsistent, duplicated surfaces that degrade point-cloud reconstruction accuracy, and (2) the oversight of visual reconstructions in selecting the most effective frontiers for exploration. Given these challenges are interrelated, we address them together by proposing a new semi-distributed framework (SPACE) for spatial cooperation in indoor environments that enables enhanced coverage and 3D mapping. SPACE leverages geometric techniques, including “mutual awareness” and a “dynamic robot filter,” to overcome spatial mapping constraints. Additionally, we introduce a novel spatial frontier detection system and map merger, integrated with an adaptive frontier assigner for optimal coverage balancing the exploration and reconstruction objectives. In extensive ROS-Gazebo simulations and real-world experiments, SPACE demonstrated superior performance over state-of-the-art approaches in both exploration and mapping metrics, demonstrating significant mitigation of the ghosting effects by multiple magnitudes. |
 | Real-World Cyber Security Demonstration for Networked Electric Drives Journal Article IEEE Journal of Emerging and Selected Topics in Power Electronics, 13 (4), 2025. Abstract | Links | BibTeX | Tags: control, networking, trust @article{Yang2025, title = {Real-World Cyber Security Demonstration for Networked Electric Drives}, author = {He Yang and Bowen Yang and Stephen Coshatt and Qi Li and Kun Hu and Bryan Cooper Hammond and Jin Ye and Ramviyas Parasuraman and Wenzhan Song}, url = {https://ieeexplore.ieee.org/document/10924153}, year = {2025}, date = {2025-08-01}, journal = {IEEE Journal of Emerging and Selected Topics in Power Electronics}, volume = {13}, number = {4}, abstract = {In this article, we present the design and implementation of a cyber-physical security testbed for networked electric drive systems, aimed at conducting real-world security demonstrations. To our knowledge, this is one of the first security testbeds for networked electric drives, seamlessly integrating the domains of power electronics and computer science, and cybersecurity. By doing so, the testbed offers a comprehensive platform to explore and understand the intricate and often complex interactions between cyber and physical systems. The core of our testbed consists of four electric machine drives, meticulously configured to emulate small-scale but realistic information technology (IT) and operational technology (OT) networks. This setup both provides a controlled environment for simulating a wide array of cyber-attacks, and mirrors potential real-world attack scenarios with a high degree of fidelity. The testbed serves as an invaluable resource for the study of cyber-physical security, offering a practical and dynamic platform for testing and validating cybersecurity measures in the context of networked electric drive systems. As a concrete example of the testbed’s capabilities, we have developed and implemented a Python-based script designed to execute step-stone attacks over a wireless local area network (WLAN). This script leverages a sequence of target IP addresses, simulating a real-world attack vector that could be exploited by adversaries. To counteract such threats, we demonstrate the efficacy of our developed cyber-attack detection algorithms, which are integral to our testbed’s security framework. Furthermore, the testbed incorporates a real-time visualization system using InfluxDB and Grafana, providing a dynamic and interactive representation of networked electric drives and their associated security monitoring mechanisms. This visualization component not only enhances the testbed’s usability but also offers insightful, real-time data for researchers and practitioners, thereby facilitating a deeper understanding of cyber-physical security dynamics in networked electric drive systems.}, keywords = {control, networking, trust}, pubstate = {published}, tppubtype = {article} } In this article, we present the design and implementation of a cyber-physical security testbed for networked electric drive systems, aimed at conducting real-world security demonstrations. To our knowledge, this is one of the first security testbeds for networked electric drives, seamlessly integrating the domains of power electronics and computer science, and cybersecurity. By doing so, the testbed offers a comprehensive platform to explore and understand the intricate and often complex interactions between cyber and physical systems. The core of our testbed consists of four electric machine drives, meticulously configured to emulate small-scale but realistic information technology (IT) and operational technology (OT) networks. This setup both provides a controlled environment for simulating a wide array of cyber-attacks, and mirrors potential real-world attack scenarios with a high degree of fidelity. The testbed serves as an invaluable resource for the study of cyber-physical security, offering a practical and dynamic platform for testing and validating cybersecurity measures in the context of networked electric drive systems. As a concrete example of the testbed’s capabilities, we have developed and implemented a Python-based script designed to execute step-stone attacks over a wireless local area network (WLAN). This script leverages a sequence of target IP addresses, simulating a real-world attack vector that could be exploited by adversaries. To counteract such threats, we demonstrate the efficacy of our developed cyber-attack detection algorithms, which are integral to our testbed’s security framework. Furthermore, the testbed incorporates a real-time visualization system using InfluxDB and Grafana, providing a dynamic and interactive representation of networked electric drives and their associated security monitoring mechanisms. This visualization component not only enhances the testbed’s usability but also offers insightful, real-time data for researchers and practitioners, thereby facilitating a deeper understanding of cyber-physical security dynamics in networked electric drive systems. |
 | Edge Computing and its Application in Robotics: A Survey Journal Article Journal of Sensor and Actuator Networks, 14 (4), 2025. Abstract | Links | BibTeX | Tags: computing, learning, multi-robot systems, networking @article{Tahir2025, title = {Edge Computing and its Application in Robotics: A Survey}, author = {Nazish Tahir and Ramviyas Parasuraman}, url = {https://www.mdpi.com/2224-2708/14/4/65}, doi = {10.3390/jsan14040065}, year = {2025}, date = {2025-06-23}, journal = {Journal of Sensor and Actuator Networks}, volume = {14}, number = {4}, abstract = {The edge computing paradigm has gained prominence in both academic and industry circles in recent years. When edge computing facilities and services are implemented in robotics, they become a key enabler in the deployment of artificial intelligence applications to robots. Time-sensitive robotics applications benefit from the reduced latency, mobility, and location awareness provided by the edge computing paradigm, which enables real-time data processing and intelligence at the network’s edge. While the advantages of integrating edge computing into robotics are numerous, there has been no recent survey that comprehensively examines these benefits. This paper aims to bridge that gap by highlighting important work in the domain of edge robotics, examining recent advancements, and offering deeper insight into the challenges and motivations behind both current and emerging solutions. In particular, this article provides a comprehensive evaluation of recent developments in edge robotics, with an emphasis on fundamental applications, providing in-depth analysis of the key motivations, challenges, and future directions in this rapidly evolving domain. It also explores the importance of edge computing in real-world robotics scenarios where rapid response times are critical. Finally, the paper outlines various open research challenges in the field of edge robotics. }, keywords = {computing, learning, multi-robot systems, networking}, pubstate = {published}, tppubtype = {article} } The edge computing paradigm has gained prominence in both academic and industry circles in recent years. When edge computing facilities and services are implemented in robotics, they become a key enabler in the deployment of artificial intelligence applications to robots. Time-sensitive robotics applications benefit from the reduced latency, mobility, and location awareness provided by the edge computing paradigm, which enables real-time data processing and intelligence at the network’s edge. While the advantages of integrating edge computing into robotics are numerous, there has been no recent survey that comprehensively examines these benefits. This paper aims to bridge that gap by highlighting important work in the domain of edge robotics, examining recent advancements, and offering deeper insight into the challenges and motivations behind both current and emerging solutions. In particular, this article provides a comprehensive evaluation of recent developments in edge robotics, with an emphasis on fundamental applications, providing in-depth analysis of the key motivations, challenges, and future directions in this rapidly evolving domain. It also explores the importance of edge computing in real-world robotics scenarios where rapid response times are critical. Finally, the paper outlines various open research challenges in the field of edge robotics. |
 | IKT-BT: Indirect Knowledge Transfer Behavior Tree Framework for Multirobot Systems Through Communication Eavesdropping Journal Article IEEE Transactions on Cybernetics, 2025. Abstract | Links | BibTeX | Tags: behavior-trees, cooperation, multi-robot @article{Venkata2025, title = {IKT-BT: Indirect Knowledge Transfer Behavior Tree Framework for Multirobot Systems Through Communication Eavesdropping}, author = {Sanjay Sarma Oruganti Venkata and Ramviyas Parasuraman and Ramana Pidaparti}, url = {https://ieeexplore.ieee.org/document/10976677}, doi = {10.1109/TCYB.2025.3560564}, year = {2025}, date = {2025-04-25}, journal = {IEEE Transactions on Cybernetics}, abstract = {Multiagent and multirobot systems (MRS) often rely on direct communication for information sharing. This work explores an alternative approach inspired by eavesdropping mechanisms in nature that involves casual observation of agent interactions to enhance decentralized knowledge dissemination. We achieve this through a novel indirect knowledge transfer through behavior trees (IKT-BT) framework tailored for a behavior-based MRS, encapsulating knowledge and control actions in behavior trees (BT). We present two new BT-based modalities—eavesdrop-update (EU) and eavesdrop-buffer-update (EBU)—incorporating unique eavesdropping strategies and efficient episodic memory management suited for resource-limited swarm robots. We theoretically analyze the IKT-BT framework for an MRS and validate the performance of the proposed modalities through extensive experiments simulating a search and rescue mission. Our results reveal improvements in both global mission performance outcomes and agent-level knowledge dissemination with a reduced need for direct communication.}, keywords = {behavior-trees, cooperation, multi-robot}, pubstate = {published}, tppubtype = {article} } Multiagent and multirobot systems (MRS) often rely on direct communication for information sharing. This work explores an alternative approach inspired by eavesdropping mechanisms in nature that involves casual observation of agent interactions to enhance decentralized knowledge dissemination. We achieve this through a novel indirect knowledge transfer through behavior trees (IKT-BT) framework tailored for a behavior-based MRS, encapsulating knowledge and control actions in behavior trees (BT). We present two new BT-based modalities—eavesdrop-update (EU) and eavesdrop-buffer-update (EBU)—incorporating unique eavesdropping strategies and efficient episodic memory management suited for resource-limited swarm robots. We theoretically analyze the IKT-BT framework for an MRS and validate the performance of the proposed modalities through extensive experiments simulating a search and rescue mission. Our results reveal improvements in both global mission performance outcomes and agent-level knowledge dissemination with a reduced need for direct communication. |
 | Online Adaptive Anomaly Detection in Networked Electrical Machines by Adaptive Enveloped Singular Spectrum Transformation Journal Article IEEE Internet of Things Journal, 12 (6), pp. 6457-646, 2025. Abstract | Links | BibTeX | Tags: control, networking @article{Wu2024, title = {Online Adaptive Anomaly Detection in Networked Electrical Machines by Adaptive Enveloped Singular Spectrum Transformation}, author = {Wu, Shushan and Yang, Bowen and Yang, He and Coshatt, Stephen J. and Gong, Xilin and Parasuraman, Ramviyas Nattanmai and Conrad, Justin and Perdisci, Roberto and Zhong, Wenxuan and Ye, Jin and Ma, Ping and Song, WenZhan}, url = {https://ieeexplore.ieee.org/abstract/document/10769069}, doi = {10.1109/JIOT.2024.3476268}, year = {2025}, date = {2025-03-15}, journal = {IEEE Internet of Things Journal}, volume = {12}, number = {6}, pages = {6457-646}, abstract = {The emergence of networked electrical machines has increased susceptibility to anomalies, including cyber-attack and physical faults, potentially leading to significant operational disruptions. In this article, we propose an online adaptive anomaly detection algorithm, adaptive enveloped singular spectrum transformation (AdaESST), which aims to identify hard-to-detect anomalies effectively. AdaESST first extracts informative components of signals by embedding the waveform data into subspaces using singular value decomposition, and then calculates anomalous score based on the subspace distance between two subsequence time series. AdaESST outperforms traditional detection methods by its capacity to adjust to new operational scenarios, thereby offering persistent protection in dynamic industrial environments. Throughout all numerical experiments simulating real-world industrial conditions, AdaESST exhibits high detection accuracy in monitoring motor and point of common coupling (PCC) currents, demonstrating its capability to safeguard against sophisticated anomalies. The detection accuracy for PCC currents is on par with that for motor currents. In essence, AdaESST has the potential to reduce the requirements for sensors, thereby lowering maintenance costs while maintaining high data integrity and security. The work contributes to enhancing the security of networked electrical machines, presenting a resilient and cost-efficient strategy in the face of emerging anomalies. }, keywords = {control, networking}, pubstate = {published}, tppubtype = {article} } The emergence of networked electrical machines has increased susceptibility to anomalies, including cyber-attack and physical faults, potentially leading to significant operational disruptions. In this article, we propose an online adaptive anomaly detection algorithm, adaptive enveloped singular spectrum transformation (AdaESST), which aims to identify hard-to-detect anomalies effectively. AdaESST first extracts informative components of signals by embedding the waveform data into subspaces using singular value decomposition, and then calculates anomalous score based on the subspace distance between two subsequence time series. AdaESST outperforms traditional detection methods by its capacity to adjust to new operational scenarios, thereby offering persistent protection in dynamic industrial environments. Throughout all numerical experiments simulating real-world industrial conditions, AdaESST exhibits high detection accuracy in monitoring motor and point of common coupling (PCC) currents, demonstrating its capability to safeguard against sophisticated anomalies. The detection accuracy for PCC currents is on par with that for motor currents. In essence, AdaESST has the potential to reduce the requirements for sensors, thereby lowering maintenance costs while maintaining high data integrity and security. The work contributes to enhancing the security of networked electrical machines, presenting a resilient and cost-efficient strategy in the face of emerging anomalies. |
 | Autonomous Navigation of a Quadruped Robot to Approach Floor Eggs and Path Optimization Analysis for Commercial Feasibility Journal Article American Society of Agricultural and Biological Engineers, 41 (6), pp. 733-747, 2025. Abstract | Links | BibTeX | Tags: autonomy, control, navigation, perception, planning @article{Mandiga2025, title = {Autonomous Navigation of a Quadruped Robot to Approach Floor Eggs and Path Optimization Analysis for Commercial Feasibility}, author = {Aravind Mandiga, Guoming Li, Tianming Liu, Ramviyas Parasuraman, Ramana M Pidaparti, Venkat UC Bodempudi, and Samuel E Aggrey}, url = {https://elibrary.asabe.org/abstract.asp?AID=55713}, doi = {10.13031/aea.16384}, year = {2025}, date = {2025-01-01}, journal = {American Society of Agricultural and Biological Engineers}, volume = {41}, number = {6}, pages = {733-747}, abstract = { Floor eggs (i.e., eggs laid on the litter floor) are a major problem in cage-free hen systems and account for approximately 5% to 6% of daily egg production. Floor eggs may be contaminated and pecked by birds, which can induce egg eating, degradation of egg quality, and risk of additional floor eggs if not collected in a timely manner. Currently, floor eggs require time-consuming manual collection in daily flock inspection. The objective was to develop autonomous navigation for a quadruped robot to approach floor eggs and to evaluate commercial feasibility through optimized routing strategies. The robot was equipped with an RGB-Depth camera for object detection and depth estimation, and multiple deep learning object detection models were evaluated. Mathematical operations associated with imagery coordinates are converted to real-world trajectories for robot movement controls. The robot was tested at speeds of 0.27, 0.34, 0.41, 0.52, and 0.68 m/s to approach floor eggs. Results show the model successfully localizes floor eggs and hens with over 95% precision, recall, and mAP50(B). The robot approaches floor eggs with an average accuracy of 90%. Commercial feasibility was assessed through mathematical optimization analysis using boustrophedon cellular decomposition for two payload scenarios (50 and 77 eggs) in a typical 50,000-hen facility (380 x 18.2 m). Optimization analysis demonstrated operational viability with total daily travel distances of 10.7 km (50-egg payload) and 7.8 km (77-egg payload) for seven daily charge cycles, successfully transferring 2,000 floor eggs to the conveyor belts. These findings show great potential for quadruped robot navigation and commercial implementation for floor egg collection.}, keywords = {autonomy, control, navigation, perception, planning}, pubstate = {published}, tppubtype = {article} } Floor eggs (i.e., eggs laid on the litter floor) are a major problem in cage-free hen systems and account for approximately 5% to 6% of daily egg production. Floor eggs may be contaminated and pecked by birds, which can induce egg eating, degradation of egg quality, and risk of additional floor eggs if not collected in a timely manner. Currently, floor eggs require time-consuming manual collection in daily flock inspection. The objective was to develop autonomous navigation for a quadruped robot to approach floor eggs and to evaluate commercial feasibility through optimized routing strategies. The robot was equipped with an RGB-Depth camera for object detection and depth estimation, and multiple deep learning object detection models were evaluated. Mathematical operations associated with imagery coordinates are converted to real-world trajectories for robot movement controls. The robot was tested at speeds of 0.27, 0.34, 0.41, 0.52, and 0.68 m/s to approach floor eggs. Results show the model successfully localizes floor eggs and hens with over 95% precision, recall, and mAP50(B). The robot approaches floor eggs with an average accuracy of 90%. Commercial feasibility was assessed through mathematical optimization analysis using boustrophedon cellular decomposition for two payload scenarios (50 and 77 eggs) in a typical 50,000-hen facility (380 x 18.2 m). Optimization analysis demonstrated operational viability with total daily travel distances of 10.7 km (50-egg payload) and 7.8 km (77-egg payload) for seven daily charge cycles, successfully transferring 2,000 floor eggs to the conveyor belts. These findings show great potential for quadruped robot navigation and commercial implementation for floor egg collection. |
 | 2025 IEEE International Symposium on Multi-Robot and Multi-Agent Systems (MRS), 2025. Abstract | Links | BibTeX | Tags: learning, localization, mapping, multi-robot systems, perception @conference{Ghanta2025c, title = {Policies over Poses: Reinforcement Learning based Distributed Pose-Graph Optimization for Multi-Robot SLAM}, author = {Sai Krishna Ghanta and Ramviyas Parasuraman}, url = {https://ieeexplore.ieee.org/document/11357260}, doi = {10.1109/MRS66243.2025.11357260}, year = {2025}, date = {2025-12-04}, booktitle = {2025 IEEE International Symposium on Multi-Robot and Multi-Agent Systems (MRS)}, abstract = {We consider the distributed pose-graph optimization (PGO) problem, which is fundamental in accurate trajectory estimation in multi-robot simultaneous localization and mapping (SLAM). Conventional iterative approaches linearize a highly non-convex optimization objective, requiring repeated solving of normal equations, which often converge to local minima and thus produce suboptimal estimates. We propose a scalable, outlier-robust distributed planar PGO framework using Multi-Agent Reinforcement Learning (MARL). We cast distributed PGO as a partially observable Markov game defined on local pose-graphs, where each action refines a single edge's pose estimate. A graph partitioner decomposes the global pose graph, and each robot runs a recurrent edge-conditioned Graph Neural Network (GNN) encoder with adaptive edge-gating to denoise noisy edges. Robots sequentially refine poses through a hybrid policy that utilizes prior action memory and graph embeddings. After local graph correction, a consensus scheme reconciles inter-robot disagreements to produce a globally consistent estimate. Our extensive evaluations on a comprehensive suite of synthetic and real-world datasets demonstrate that our learned MARL-based actors reduce the global objective by an average of 37.5% more than the state-of-the-art distributed PGO framework, while enhancing inference efficiency by at least 6X. We also demonstrate that actor replication allows a single learned policy to scale effortlessly to substantially larger robot teams without any retraining. Code is publicly available at https://github.com/herolab-uga/policies-over-poses }, keywords = {learning, localization, mapping, multi-robot systems, perception}, pubstate = {published}, tppubtype = {conference} } We consider the distributed pose-graph optimization (PGO) problem, which is fundamental in accurate trajectory estimation in multi-robot simultaneous localization and mapping (SLAM). Conventional iterative approaches linearize a highly non-convex optimization objective, requiring repeated solving of normal equations, which often converge to local minima and thus produce suboptimal estimates. We propose a scalable, outlier-robust distributed planar PGO framework using Multi-Agent Reinforcement Learning (MARL). We cast distributed PGO as a partially observable Markov game defined on local pose-graphs, where each action refines a single edge's pose estimate. A graph partitioner decomposes the global pose graph, and each robot runs a recurrent edge-conditioned Graph Neural Network (GNN) encoder with adaptive edge-gating to denoise noisy edges. Robots sequentially refine poses through a hybrid policy that utilizes prior action memory and graph embeddings. After local graph correction, a consensus scheme reconciles inter-robot disagreements to produce a globally consistent estimate. Our extensive evaluations on a comprehensive suite of synthetic and real-world datasets demonstrate that our learned MARL-based actors reduce the global objective by an average of 37.5% more than the state-of-the-art distributed PGO framework, while enhancing inference efficiency by at least 6X. We also demonstrate that actor replication allows a single learned policy to scale effortlessly to substantially larger robot teams without any retraining. Code is publicly available at https://github.com/herolab-uga/policies-over-poses |
 | Anonymous Distributed Localisation via Spatial Population Protocols Conference International Symposium on Algorithms and Computation (ISAAC 2025)., 2025. Links | BibTeX | Tags: localization, multi-robot systems, networking @conference{Gąsieniec2025b, title = {Anonymous Distributed Localisation via Spatial Population Protocols}, author = {Leszek Gąsieniec, Łukasz Kuszner, Ehsan Latif, Parasuraman, Ramviyas, Paul Spirakis, and Grzegorz Stachowiak}, url = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ISAAC.2025.35}, doi = {10.4230/LIPIcs.ISAAC.2025.35}, year = {2025}, date = {2025-11-17}, booktitle = {International Symposium on Algorithms and Computation (ISAAC 2025).}, keywords = {localization, multi-robot systems, networking}, pubstate = {published}, tppubtype = {conference} } |
 | Analyzing Human Perceptions of a MEDEVAC Robot in a Simulated Evacuation Scenario Conference 2025 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2025. Abstract | Links | BibTeX | Tags: autonomy, human-robot interaction, navigation, trust @conference{Goodie2025, title = {Analyzing Human Perceptions of a MEDEVAC Robot in a Simulated Evacuation Scenario}, author = {Tyson Jordan; Pranav Pandey; Prashant Doshi; Ramviyas Parasuraman; Adam Goodie}, url = {https://ieeexplore.ieee.org/document/11246558}, doi = {10.1109/IROS60139.2025.11246558}, year = {2025}, date = {2025-10-19}, booktitle = {2025 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}, abstract = {The use of autonomous systems in medical evacuation (MEDEVAC) scenarios is promising, but existing implementations overlook key insights from human-robot interaction (HRI) research. Studies on human-machine teams demonstrate that human perceptions of a machine teammate are critical in governing the machine’s performance. Consequently, it is essential to identify the factors that contribute to positive human perceptions in human-machine teams. Here, we present a mixed factorial design to assess human perceptions of a MEDEVAC robot in a simulated evacuation scenario. Participants were assigned to the role of casualty (CAS) or bystander (BYS) and subjected to three within-subjects conditions based on the MEDEVAC robot’s operating mode: autonomous-slow (AS), autonomous-fast (AF), and teleoperation (TO). During each trial, a MEDEVAC robot navigated an 11-meter path, acquiring a casualty and transporting them to an ambulance exchange point while avoiding an idle bystander. Following each trial, subjects completed a questionnaire measuring their emotional states, perceived safety, and social compatibility with the robot. Results indicate a consistent main effect of operating mode on reported emotional states and perceived safety. Pairwise analyses suggest that the employment of the AF operating mode negatively impacted perceptions along these dimensions. There were no persistent differences between CAS and BYS responses. }, keywords = {autonomy, human-robot interaction, navigation, trust}, pubstate = {published}, tppubtype = {conference} } The use of autonomous systems in medical evacuation (MEDEVAC) scenarios is promising, but existing implementations overlook key insights from human-robot interaction (HRI) research. Studies on human-machine teams demonstrate that human perceptions of a machine teammate are critical in governing the machine’s performance. Consequently, it is essential to identify the factors that contribute to positive human perceptions in human-machine teams. Here, we present a mixed factorial design to assess human perceptions of a MEDEVAC robot in a simulated evacuation scenario. Participants were assigned to the role of casualty (CAS) or bystander (BYS) and subjected to three within-subjects conditions based on the MEDEVAC robot’s operating mode: autonomous-slow (AS), autonomous-fast (AF), and teleoperation (TO). During each trial, a MEDEVAC robot navigated an 11-meter path, acquiring a casualty and transporting them to an ambulance exchange point while avoiding an idle bystander. Following each trial, subjects completed a questionnaire measuring their emotional states, perceived safety, and social compatibility with the robot. Results indicate a consistent main effect of operating mode on reported emotional states and perceived safety. Pairwise analyses suggest that the employment of the AF operating mode negatively impacted perceptions along these dimensions. There were no persistent differences between CAS and BYS responses. |
 | Distributed Fault-Tolerant Multi-Robot Cooperative Localization in Adversarial Environments Conference 2025 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2025. Abstract | Links | BibTeX | Tags: cooperation, localization, multi-robot systems @conference{Tasooji2025, title = {Distributed Fault-Tolerant Multi-Robot Cooperative Localization in Adversarial Environments}, author = {Tohid Tasooji and Ramviyas Parasuraman}, url = {https://ieeexplore.ieee.org/document/11246042}, doi = {10.1109/IROS60139.2025.11246042}, year = {2025}, date = {2025-10-19}, booktitle = {2025 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}, abstract = {In multi-robot systems (MRS), cooperative localization is a crucial task for enhancing system robustness and scalability, especially in GPS-denied or communication-limited environments. However, adversarial attacks, such as sensor manipulation, and communication jamming, pose significant challenges to the performance of traditional localization methods. In this paper, we propose a novel distributed fault-tolerant cooperative localization framework to enhance resilience against sensor and communication disruptions in adversarial environments. We introduce an adaptive event-triggered communication strategy that dynamically adjusts communication thresholds based on real-time sensing and communication quality. This strategy ensures optimal performance even in the presence of sensor degradation or communication failure. Furthermore, we conduct a rigorous analysis of the convergence and stability properties of the proposed algorithm, demonstrating its resilience against bounded adversarial zones and maintaining accurate state estimation. Robotarium-based experiment results show that our proposed algorithm significantly outperforms traditional methods in terms of localization accuracy and communication efficiency, particularly in adversarial settings. Our approach offers improved scalability, reliability, and fault tolerance for MRS, making it suitable for large-scale deployments in real-world, challenging environments. }, keywords = {cooperation, localization, multi-robot systems}, pubstate = {published}, tppubtype = {conference} } In multi-robot systems (MRS), cooperative localization is a crucial task for enhancing system robustness and scalability, especially in GPS-denied or communication-limited environments. However, adversarial attacks, such as sensor manipulation, and communication jamming, pose significant challenges to the performance of traditional localization methods. In this paper, we propose a novel distributed fault-tolerant cooperative localization framework to enhance resilience against sensor and communication disruptions in adversarial environments. We introduce an adaptive event-triggered communication strategy that dynamically adjusts communication thresholds based on real-time sensing and communication quality. This strategy ensures optimal performance even in the presence of sensor degradation or communication failure. Furthermore, we conduct a rigorous analysis of the convergence and stability properties of the proposed algorithm, demonstrating its resilience against bounded adversarial zones and maintaining accurate state estimation. Robotarium-based experiment results show that our proposed algorithm significantly outperforms traditional methods in terms of localization accuracy and communication efficiency, particularly in adversarial settings. Our approach offers improved scalability, reliability, and fault tolerance for MRS, making it suitable for large-scale deployments in real-world, challenging environments. |
 | 2025 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2025. Abstract | Links | BibTeX | Tags: cooperation, localization, multi-robot systems, networking, perception @conference{Ghanta2025b, title = {MGPRL: Distributed Multi-Gaussian Processes for Wi-Fi-based Multi-Robot Relative Localization in Large Indoor Environments}, author = {Sai Krishna Ghanta and Ramviyas Parasuraman}, url = {https://ieeexplore.ieee.org/document/11247180}, doi = {10.1109/IROS60139.2025.11247180}, year = {2025}, date = {2025-10-19}, booktitle = {2025 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}, abstract = {Relative localization is a crucial capability for multi-robot systems operating in GPS-denied environments. Existing approaches for multi-robot relative localization often depend on costly or short-range sensors like cameras and LiDARs. Consequently, these approaches face challenges such as high computational overhead (e.g., map merging) and difficulties in disjoint environments. To address this limitation, this paper introduces MGPRL, a novel distributed framework for multi-robot relative localization using convex-hull of multiple Wi-Fi access points (AP). To accomplish this, we employ co-regionalized multi-output Gaussian Processes for efficient Radio Signal Strength Indicator (RSSI) field prediction and perform uncertainty-aware multi-AP localization, which is further coupled with weighted convex hull-based alignment for robust relative pose estimation. Each robot predicts the RSSI field of the environment by an online scan of APs in its environment, which are utilized for position estimation of multiple APs. To perform relative localization, each robot aligns the convex hull of its predicted AP locations with that of the neighbor robots. This approach is well-suited for devices with limited computational resources and operates solely on widely available Wi-Fi RSSI measurements without necessitating any dedicated pre-calibration or offline fingerprinting. We rigorously evaluate the performance of the proposed MGPRL in ROS simulations and demonstrate it with real-world experiments, comparing it against multiple state-of-the-art approaches. The results showcase that MGPRL outperforms existing methods in terms of localization accuracy and computational efficiency.}, keywords = {cooperation, localization, multi-robot systems, networking, perception}, pubstate = {published}, tppubtype = {conference} } Relative localization is a crucial capability for multi-robot systems operating in GPS-denied environments. Existing approaches for multi-robot relative localization often depend on costly or short-range sensors like cameras and LiDARs. Consequently, these approaches face challenges such as high computational overhead (e.g., map merging) and difficulties in disjoint environments. To address this limitation, this paper introduces MGPRL, a novel distributed framework for multi-robot relative localization using convex-hull of multiple Wi-Fi access points (AP). To accomplish this, we employ co-regionalized multi-output Gaussian Processes for efficient Radio Signal Strength Indicator (RSSI) field prediction and perform uncertainty-aware multi-AP localization, which is further coupled with weighted convex hull-based alignment for robust relative pose estimation. Each robot predicts the RSSI field of the environment by an online scan of APs in its environment, which are utilized for position estimation of multiple APs. To perform relative localization, each robot aligns the convex hull of its predicted AP locations with that of the neighbor robots. This approach is well-suited for devices with limited computational resources and operates solely on widely available Wi-Fi RSSI measurements without necessitating any dedicated pre-calibration or offline fingerprinting. We rigorously evaluate the performance of the proposed MGPRL in ROS simulations and demonstrate it with real-world experiments, comparing it against multiple state-of-the-art approaches. The results showcase that MGPRL outperforms existing methods in terms of localization accuracy and computational efficiency. |
 | Integrating Perceptions: A Human-Centered Physical Safety Model for Human-Robot Interaction Conference 2025 34th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), 2025. Abstract | Links | BibTeX | Tags: cooperation, human-robot interaction, navigation, trust @conference{Pandey2025, title = {Integrating Perceptions: A Human-Centered Physical Safety Model for Human-Robot Interaction}, author = {Pranav Kumar Pandey, Ramviyas Parasuraman, and Prashant Doshi}, url = {https://ieeexplore.ieee.org/document/11217747}, doi = {10.1109/RO-MAN63969.2025.11217747}, year = {2025}, date = {2025-08-25}, booktitle = {2025 34th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN)}, abstract = {Ensuring safety in human-robot interaction (HRI) is essential to foster user trust and enable the broader adoption of robotic systems. Traditional safety models primarily rely on sensor-based measures, such as relative distance and velocity, to assess physical safety. However, these models often fail to capture subjective safety perceptions, which are shaped by individual traits and contextual factors. In this paper, we introduce and analyze a parameterized general safety model that bridges the gap between physical and perceived safety by incorporating a personalization parameter, ρ, into the safety measurement framework to account for individual differences in safety perception. Through a series of hypothesis-driven human-subject studies in a simulated rescue scenario, we investigate how emotional state, trust, and robot behavior influence perceived safety. Our results show that ρ effectively captures meaningful individual differences, driven by affective responses, trust in task consistency, and clustering into distinct user types. Specifically, our findings confirm that predictable and consistent robot behavior as well as the elicitation of positive emotional states, significantly enhance perceived safety. Moreover, responses cluster into a small number of user types, supporting adaptive personalization based on shared safety models. Notably, participant role significantly shapes safety perception, and repeated exposure reduces perceived safety for participants in the casualty role, emphasizing the impact of physical interaction and experiential change. These findings highlight the importance of adaptive, human-centered safety models that integrate both psychological and behavioral dimensions, offering a pathway toward more trustworthy and effective HRI in safety-critical domains. }, keywords = {cooperation, human-robot interaction, navigation, trust}, pubstate = {published}, tppubtype = {conference} } Ensuring safety in human-robot interaction (HRI) is essential to foster user trust and enable the broader adoption of robotic systems. Traditional safety models primarily rely on sensor-based measures, such as relative distance and velocity, to assess physical safety. However, these models often fail to capture subjective safety perceptions, which are shaped by individual traits and contextual factors. In this paper, we introduce and analyze a parameterized general safety model that bridges the gap between physical and perceived safety by incorporating a personalization parameter, ρ, into the safety measurement framework to account for individual differences in safety perception. Through a series of hypothesis-driven human-subject studies in a simulated rescue scenario, we investigate how emotional state, trust, and robot behavior influence perceived safety. Our results show that ρ effectively captures meaningful individual differences, driven by affective responses, trust in task consistency, and clustering into distinct user types. Specifically, our findings confirm that predictable and consistent robot behavior as well as the elicitation of positive emotional states, significantly enhance perceived safety. Moreover, responses cluster into a small number of user types, supporting adaptive personalization based on shared safety models. Notably, participant role significantly shapes safety perception, and repeated exposure reduces perceived safety for participants in the casualty role, emphasizing the impact of physical interaction and experiential change. These findings highlight the importance of adaptive, human-centered safety models that integrate both psychological and behavioral dimensions, offering a pathway toward more trustworthy and effective HRI in safety-critical domains. |
 | Brief Announcement: Anonymous Distributed Localisation via Spatial Population Protocols Conference 4th Symposium on Algorithmic Foundations of Dynamic Networks (SAND 2025), 2025. Abstract | Links | BibTeX | Tags: multi-robot systems, networking @conference{Gąsieniec2025, title = {Brief Announcement: Anonymous Distributed Localisation via Spatial Population Protocols}, author = { Leszek Gąsieniec, Łukasz Kuszner, Ehsan Latif, Ramviyas Parasuraman, Paul Spirakis, Grzegorz Stachowiak}, url = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.SAND.2025.19}, doi = {10.4230/LIPIcs.SAND.2025.19}, year = {2025}, date = {2025-06-02}, booktitle = {4th Symposium on Algorithmic Foundations of Dynamic Networks (SAND 2025)}, abstract = {In the distributed localization problem (DLP), n anonymous robots (agents) A₀, …, A_{n-1} begin at arbitrary positions p₀, …, p_{n-1} ∈ S, where S is a Euclidean space. Initially, each agent A_i operates within its own coordinate system in S, which may be inconsistent with those of other agents. The primary goal in DLP is for agents to reach a consensus on a unified coordinate system that accurately reflects the relative positions of all points, p₀, …, p_{n-1}, in S. Extensive research on DLP has primarily focused on the feasibility and complexity of achieving consensus when agents have limited access to inter-agent distances, often due to missing or imprecise data. In this paper, however, we examine a minimalist, computationally efficient model of distributed computing in which agents have access to all pairwise distances, if needed. Specifically, we introduce a novel variant of population protocols, referred to as the spatial population protocols model. In this variant each agent can memorise one or a fixed number of coordinates, and when agents A_i and A_j interact, they can not only exchange their current knowledge but also either determine the distance d_{ij} between them in S (distance query model) or obtain the vector v→_{ij} spanning points p_i and p_j (vector query model). We present here a leader-based localisation protocol with distance queries. }, keywords = {multi-robot systems, networking}, pubstate = {published}, tppubtype = {conference} } In the distributed localization problem (DLP), n anonymous robots (agents) A₀, …, A_{n-1} begin at arbitrary positions p₀, …, p_{n-1} ∈ S, where S is a Euclidean space. Initially, each agent A_i operates within its own coordinate system in S, which may be inconsistent with those of other agents. The primary goal in DLP is for agents to reach a consensus on a unified coordinate system that accurately reflects the relative positions of all points, p₀, …, p_{n-1}, in S. Extensive research on DLP has primarily focused on the feasibility and complexity of achieving consensus when agents have limited access to inter-agent distances, often due to missing or imprecise data. In this paper, however, we examine a minimalist, computationally efficient model of distributed computing in which agents have access to all pairwise distances, if needed. Specifically, we introduce a novel variant of population protocols, referred to as the spatial population protocols model. In this variant each agent can memorise one or a fixed number of coordinates, and when agents A_i and A_j interact, they can not only exchange their current knowledge but also either determine the distance d_{ij} between them in S (distance query model) or obtain the vector v→_{ij} spanning points p_i and p_j (vector query model). We present here a leader-based localisation protocol with distance queries. |
 | 2025 IEEE International Conference on Robotics and Automation (ICRA), 2025. Abstract | Links | BibTeX | Tags: control, cooperation, multi-robot, planning @conference{Starks2025, title = {GMF: Gravitational Mass-Force Framework for Parametric Multi-Level Coordination In Multi-Robot and Swarm Robotic Systems}, author = {Michael Starks and Ramviyas Parasuraman}, url = {https://ieeexplore.ieee.org/document/11128543/}, doi = {10.1109/ICRA55743.2025.11128543}, year = {2025}, date = {2025-05-23}, booktitle = {2025 IEEE International Conference on Robotics and Automation (ICRA)}, abstract = {Distributed multi-robot coordination is critical to achieving reliable robotic missions that exploit the collective capability of swarm robots. In particular, the consensus and formation control problems have been extensively studied, resulting in distributed controllers that enable robots to rely only on information from themselves and their immediate neighbors. However, these algorithms are usually designed for specific objectives (e.g., cooperative object transportation, environmental coverage, etc.), requiring the controllers to be re-designed for domain variations. Therefore, we propose a new parametric framework inspired by gravitational fields that allow simultaneous coordination of robots at multiple levels, enabling generalization and domain adaptation. Our approach is built on top of a connectivity-preserving formation controller, with need-based and task-based ad hoc coordination at private, local, and global layers of a swarm robot team. We demonstrate the remarkable potential of our framework through extensive simulations and real-world swarm robot experiments in three representative multi-robot tasks involving tight coordination: 1) robot-initiated rendezvous at different coordination layers, 2) coordinated boundary tracking and coverage of environmental processes, and 3) accommodating task executions and motion control while satisfying the coordination laws.}, keywords = {control, cooperation, multi-robot, planning}, pubstate = {published}, tppubtype = {conference} } Distributed multi-robot coordination is critical to achieving reliable robotic missions that exploit the collective capability of swarm robots. In particular, the consensus and formation control problems have been extensively studied, resulting in distributed controllers that enable robots to rely only on information from themselves and their immediate neighbors. However, these algorithms are usually designed for specific objectives (e.g., cooperative object transportation, environmental coverage, etc.), requiring the controllers to be re-designed for domain variations. Therefore, we propose a new parametric framework inspired by gravitational fields that allow simultaneous coordination of robots at multiple levels, enabling generalization and domain adaptation. Our approach is built on top of a connectivity-preserving formation controller, with need-based and task-based ad hoc coordination at private, local, and global layers of a swarm robot team. We demonstrate the remarkable potential of our framework through extensive simulations and real-world swarm robot experiments in three representative multi-robot tasks involving tight coordination: 1) robot-initiated rendezvous at different coordination layers, 2) coordinated boundary tracking and coverage of environmental processes, and 3) accommodating task executions and motion control while satisfying the coordination laws. |
 | GSI- A Proxemics-Guided Generalized Safety Metric For Evaluating Safety in Social Navigation Context Workshop IEEE ICRA 2025 Workshop on Advances in Social Navigation: Planning, HRI and Beyond, 2025, (Received Best Poster Award.). Links | BibTeX | Tags: human-robot interaction, navigation, trust @workshop{Pandey2025b, title = {GSI- A Proxemics-Guided Generalized Safety Metric For Evaluating Safety in Social Navigation Context}, author = {Pranav Pandey, Ramviyas Parasuraman, and Prashant Doshi.}, url = {https://socialnav2025.pages.dev/papers/GSI-%20A%20Proxemics-Guided%20Generalized%20Safety%20Metric%20For%20Evaluating%20Safety%20in%20Social%20Navigation%20Context.pdf}, year = {2025}, date = {2025-05-19}, booktitle = {IEEE ICRA 2025 Workshop on Advances in Social Navigation: Planning, HRI and Beyond}, note = {Received Best Poster Award.}, keywords = {human-robot interaction, navigation, trust}, pubstate = {published}, tppubtype = {workshop} } |
 | H-Cov: Multi-UAV Sensor Coverage with Altitude Optimization for Target Tracking Workshop IEEE ICRA 2025 Workshop on 25 YEARS OF AERIAL ROBOTICS: CHALLENGES AND OPPORTUNITIES, 2025. Abstract | Links | BibTeX | Tags: autonomy, control, multi-robot systems, planning @workshop{Nistane2025, title = {H-Cov: Multi-UAV Sensor Coverage with Altitude Optimization for Target Tracking}, author = {Swaraj Nistane, Tohid Tasooji, and Ramviyas Parasuraman}, url = {https://aerial-robotics-workshop-icra.com/wp-content/uploads/2025/05/Poster14.pdf}, year = {2025}, date = {2025-05-19}, booktitle = {IEEE ICRA 2025 Workshop on 25 YEARS OF AERIAL ROBOTICS: CHALLENGES AND OPPORTUNITIES}, abstract = {This paper presents a distributed multi-target coverage control framework for multiple unmanned aerial vehicle (UAV) systems that integrates Voronoi-based coverage control with altitude optimization. The proposed method enables robots to adjust their positions and altitudes dynamically to optimize sensing performance across varying target distributions. The framework effectively balances trade-offs between detection costs and coverage area by determining the optimal position and altitude for each robot. This allows the system to adapt to different environmental conditions and target densities, ensuring optimal performance in various scenarios. In the proposed framework, robots descend to lower altitudes in high-density target regions to improve detection accuracy, while in sparse regions, they ascend to maximize coverage. A minimum altitude constraint is obtained to maintain precise tracking in dense areas, ensuring that robots do not operate at excessively low altitudes. The approach guarantees complete coverage of the target space by guiding robots toward the weighted centroids of their respective Voronoi cells, thereby ensuring efficient task allocation and spatial distribution. Simulation experiments demonstrate the effectiveness of the proposed framework in improving tracking accuracy and coverage efficiency in different environments. The results validate the capability of the framework to handle real-time, multi-target tracking and sensor coverage in complex target distributions.}, keywords = {autonomy, control, multi-robot systems, planning}, pubstate = {published}, tppubtype = {workshop} } This paper presents a distributed multi-target coverage control framework for multiple unmanned aerial vehicle (UAV) systems that integrates Voronoi-based coverage control with altitude optimization. The proposed method enables robots to adjust their positions and altitudes dynamically to optimize sensing performance across varying target distributions. The framework effectively balances trade-offs between detection costs and coverage area by determining the optimal position and altitude for each robot. This allows the system to adapt to different environmental conditions and target densities, ensuring optimal performance in various scenarios. In the proposed framework, robots descend to lower altitudes in high-density target regions to improve detection accuracy, while in sparse regions, they ascend to maximize coverage. A minimum altitude constraint is obtained to maintain precise tracking in dense areas, ensuring that robots do not operate at excessively low altitudes. The approach guarantees complete coverage of the target space by guiding robots toward the weighted centroids of their respective Voronoi cells, thereby ensuring efficient task allocation and spatial distribution. Simulation experiments demonstrate the effectiveness of the proposed framework in improving tracking accuracy and coverage efficiency in different environments. The results validate the capability of the framework to handle real-time, multi-target tracking and sensor coverage in complex target distributions. |
 | IEEE ICRA 2025 Workshop on Block by Block Collaborative Strategies for Multi-agent Robotic Construction, 2025. Abstract | Links | BibTeX | Tags: mapping, multi-robot systems, perception, planning @workshop{Ghanta2025d, title = {SPACE: 3D Spatial Co-operation and Exploration Framework for Robust Mapping and Coverage with Multi-Robot Systems}, author = {Sai Krishna Ghanta and Ramviyas Parasuraman}, url = {https://cearlab.github.io/blockbyblock.github.io/index.html}, year = {2025}, date = {2025-05-19}, booktitle = {IEEE ICRA 2025 Workshop on Block by Block Collaborative Strategies for Multi-agent Robotic Construction}, abstract = {Multi-robot systems hold promise for accelerating cooperative construction tasks such as site preparation and modular assembly. However, dynamic inter-robot occlusions in 3D point-cloud mapping introduce ghosting artifacts that compromise surface reconstruction accuracy and impede downstream planning for grading and leveling. Furthermore, traditional 2D grid-based frontier approaches fail to capture volumetric nuances in partially reconstructed areas, limiting exploration. We propose SPACE, a semi-distributed framework that (1) employs geometric mutual-awareness coupled with image-plane clustering to suppress dynamic robot artifacts, and (2) introduces a bi-variate frontier detection and assignment scheme that classifies and prioritizes both unexplored and weakly mapped regions. SPACE achieves up to 99% reduction in ghosting volume and 95% exploration coverage in ROS-Gazebo experiments and real-world experiments. }, keywords = {mapping, multi-robot systems, perception, planning}, pubstate = {published}, tppubtype = {workshop} } Multi-robot systems hold promise for accelerating cooperative construction tasks such as site preparation and modular assembly. However, dynamic inter-robot occlusions in 3D point-cloud mapping introduce ghosting artifacts that compromise surface reconstruction accuracy and impede downstream planning for grading and leveling. Furthermore, traditional 2D grid-based frontier approaches fail to capture volumetric nuances in partially reconstructed areas, limiting exploration. We propose SPACE, a semi-distributed framework that (1) employs geometric mutual-awareness coupled with image-plane clustering to suppress dynamic robot artifacts, and (2) introduces a bi-variate frontier detection and assignment scheme that classifies and prioritizes both unexplored and weakly mapped regions. SPACE achieves up to 99% reduction in ghosting volume and 95% exploration coverage in ROS-Gazebo experiments and real-world experiments. |
2024 |
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 | Bayesian Strategy Networks Based Soft Actor-Critic Learning Journal Article ACM Transactions on Intelligent Systems and Technology, 15 (3), pp. 1–24, 2024. Abstract | Links | BibTeX | Tags: control, learning @article{Yang2024b, title = {Bayesian Strategy Networks Based Soft Actor-Critic Learning}, author = {Qin Yang and Ramviyas Parasuraman}, url = {https://dl.acm.org/doi/10.1145/3643862}, doi = {10.1145/3643862}, year = {2024}, date = {2024-03-29}, journal = {ACM Transactions on Intelligent Systems and Technology}, volume = {15}, number = {3}, pages = {1–24}, abstract = {A strategy refers to the rules that the agent chooses the available actions to achieve goals. Adopting reasonable strategies is challenging but crucial for an intelligent agent with limited resources working in hazardous, unstructured, and dynamic environments to improve the system’s utility, decrease the overall cost, and increase mission success probability. This paper proposes a novel hierarchical strategy decomposition approach based on Bayesian chaining to separate an intricate policy into several simple sub-policies and organize their relationships as Bayesian strategy networks (BSN). We integrate this approach into the state-of-the-art DRL method – soft actor-critic (SAC), and build the corresponding Bayesian soft actor-critic (BSAC) model by organizing several sub-policies as a joint policy. Our method achieves the state-of-the-art performance on the standard continuous control benchmarks in the OpenAI Gym environment. The results demonstrate that the promising potential of the BSAC method significantly improves training efficiency. Furthermore, we extend the topic to the Multi-Agent systems (MAS), discussing the potential research fields and directions.}, keywords = {control, learning}, pubstate = {published}, tppubtype = {article} } A strategy refers to the rules that the agent chooses the available actions to achieve goals. Adopting reasonable strategies is challenging but crucial for an intelligent agent with limited resources working in hazardous, unstructured, and dynamic environments to improve the system’s utility, decrease the overall cost, and increase mission success probability. This paper proposes a novel hierarchical strategy decomposition approach based on Bayesian chaining to separate an intricate policy into several simple sub-policies and organize their relationships as Bayesian strategy networks (BSN). We integrate this approach into the state-of-the-art DRL method – soft actor-critic (SAC), and build the corresponding Bayesian soft actor-critic (BSAC) model by organizing several sub-policies as a joint policy. Our method achieves the state-of-the-art performance on the standard continuous control benchmarks in the OpenAI Gym environment. The results demonstrate that the promising potential of the BSAC method significantly improves training efficiency. Furthermore, we extend the topic to the Multi-Agent systems (MAS), discussing the potential research fields and directions. |
 | Communication-Efficient Multi-Robot Exploration Using Coverage-biased Distributed Q-Learning Journal Article IEEE Robotics and Automation Letters, 9 (3), pp. 2622 - 2629, 2024. Abstract | Links | BibTeX | Tags: cooperation, learning, mapping, multi-robot, networking @article{Latif2024b, title = {Communication-Efficient Multi-Robot Exploration Using Coverage-biased Distributed Q-Learning}, author = {Ehsan Latif and Ramviyas Parasuraman}, url = {https://ieeexplore.ieee.org/document/10413563}, doi = {10.1109/LRA.2024.3358095}, year = {2024}, date = {2024-03-01}, journal = {IEEE Robotics and Automation Letters}, volume = {9}, number = {3}, pages = {2622 - 2629}, abstract = {Frontier exploration and reinforcement learning have historically been used to solve the problem of enabling many mobile robots to autonomously and cooperatively explore complex surroundings. These methods need to keep an internal global map for navigation, but they do not take into consideration the high costs of communication and information sharing between robots. This study offers CQLite, a novel distributed Q-learning technique designed to minimize data communication overhead between robots while achieving rapid convergence and thorough coverage in multi-robot exploration. The proposed CQLite method uses ad hoc map merging, and selectively shares updated Q-values at recently identified frontiers to significantly reduce communication costs. The theoretical analysis of CQLite's convergence and efficiency, together with extensive numerical verification on simulated indoor maps utilizing several robots, demonstrates the method's novelty. With over 2x reductions in computation and communication alongside improved mapping performance, CQLite outperformed cutting-edge multi-robot exploration techniques like Rapidly Exploring Random Trees and Deep Reinforcement Learning. }, keywords = {cooperation, learning, mapping, multi-robot, networking}, pubstate = {published}, tppubtype = {article} } Frontier exploration and reinforcement learning have historically been used to solve the problem of enabling many mobile robots to autonomously and cooperatively explore complex surroundings. These methods need to keep an internal global map for navigation, but they do not take into consideration the high costs of communication and information sharing between robots. This study offers CQLite, a novel distributed Q-learning technique designed to minimize data communication overhead between robots while achieving rapid convergence and thorough coverage in multi-robot exploration. The proposed CQLite method uses ad hoc map merging, and selectively shares updated Q-values at recently identified frontiers to significantly reduce communication costs. The theoretical analysis of CQLite's convergence and efficiency, together with extensive numerical verification on simulated indoor maps utilizing several robots, demonstrates the method's novelty. With over 2x reductions in computation and communication alongside improved mapping performance, CQLite outperformed cutting-edge multi-robot exploration techniques like Rapidly Exploring Random Trees and Deep Reinforcement Learning. |
 | Instantaneous Wireless Robotic Node Localization Using Collaborative Direction of Arrival Journal Article IEEE Internet of Things Journal, 11 (2), pp. 2783 - 2795, 2024. Abstract | Links | BibTeX | Tags: cooperation, localization, networking @article{Latif2023c, title = {Instantaneous Wireless Robotic Node Localization Using Collaborative Direction of Arrival}, author = {Ehsan Latif and Ramviyas Parasuraman}, url = {https://ieeexplore.ieee.org/document/10185556}, doi = {10.1109/JIOT.2023.3296334}, year = {2024}, date = {2024-01-15}, journal = {IEEE Internet of Things Journal}, volume = {11}, number = {2}, pages = {2783 - 2795}, abstract = {Localizing mobile robotic nodes in indoor and GPS-denied environments is a complex problem, particularly in dynamic, unstructured scenarios where traditional cameras and LIDAR-based sensing and localization modalities may fail. Alternatively, wireless signal-based localization has been extensively studied in the literature yet primarily focuses on fingerprinting and feature-matching paradigms, requiring dedicated environment-specific offline data collection. We propose an online robot localization algorithm enabled by collaborative wireless sensor nodes to remedy these limitations. Our approach's core novelty lies in obtaining the Collaborative Direction of Arrival (CDOA) of wireless signals by exploiting the geometric features and collaboration between wireless nodes. The CDOA is combined with the Expectation Maximization (EM) and Particle Filter (PF) algorithms to calculate the Gaussian probability of the node's location with high efficiency and accuracy. The algorithm relies on RSSI-only data, making it ubiquitous to resource-constrained devices. We theoretically analyze the approach and extensively validate the proposed method's consistency, accuracy, and computational efficiency in simulations, real-world public datasets, as well as real robot demonstrations. The results validate the method's real-time computational capability and demonstrate considerably-high centimeter-level localization accuracy, outperforming relevant state-of-the-art localization approaches. }, keywords = {cooperation, localization, networking}, pubstate = {published}, tppubtype = {article} } Localizing mobile robotic nodes in indoor and GPS-denied environments is a complex problem, particularly in dynamic, unstructured scenarios where traditional cameras and LIDAR-based sensing and localization modalities may fail. Alternatively, wireless signal-based localization has been extensively studied in the literature yet primarily focuses on fingerprinting and feature-matching paradigms, requiring dedicated environment-specific offline data collection. We propose an online robot localization algorithm enabled by collaborative wireless sensor nodes to remedy these limitations. Our approach's core novelty lies in obtaining the Collaborative Direction of Arrival (CDOA) of wireless signals by exploiting the geometric features and collaboration between wireless nodes. The CDOA is combined with the Expectation Maximization (EM) and Particle Filter (PF) algorithms to calculate the Gaussian probability of the node's location with high efficiency and accuracy. The algorithm relies on RSSI-only data, making it ubiquitous to resource-constrained devices. We theoretically analyze the approach and extensively validate the proposed method's consistency, accuracy, and computational efficiency in simulations, real-world public datasets, as well as real robot demonstrations. The results validate the method's real-time computational capability and demonstrate considerably-high centimeter-level localization accuracy, outperforming relevant state-of-the-art localization approaches. |
 | Energy-Aware Coverage Planning for Heterogeneous Multi-Robot System Conference The 17th International Symposium on Distributed Autonomous Robotic Systems (DARS) 2024, 2024, (In Press). Abstract | Links | BibTeX | Tags: control, cooperation, multi-robot, planning @conference{Munir2024b, title = {Energy-Aware Coverage Planning for Heterogeneous Multi-Robot System}, author = {Aiman Munir and Ayan Dutta and Ramviyas Parasuraman}, url = {https://link.springer.com/chapter/10.1007/978-3-032-04584-3_15}, doi = {10.1007/978-3-032-04584-3_15}, year = {2024}, date = {2024-10-31}, booktitle = {The 17th International Symposium on Distributed Autonomous Robotic Systems (DARS) 2024}, abstract = {We propose a distributed control law for a heterogeneous multi-robot coverage problem, where the robots could have different energy characteristics, such as capacity and depletion rates, due to their varying sizes, speeds, capabilities, and payloads. Existing energy-aware coverage control laws consider capacity differences but assume the battery depletion rate to be the same for all robots. In realistic scenarios, however, some robots can consume energy much faster than other robots; for instance, UAVs hover at different altitudes, and these changes could be dynamically updated based on their assigned tasks. Robots' energy capacities and depletion rates need to be considered to maximize the performance of a multi-robot system. To this end, we propose a new energy-aware controller based on Lloyd's algorithm to adapt the weights of the robots based on their energy dynamics and divide the area of interest among the robots accordingly. The controller is theoretically analyzed and extensively evaluated through simulations and real-world demonstrations in multiple realistic scenarios and compared with three baseline control laws to validate its performance and efficacy.}, note = {In Press}, keywords = {control, cooperation, multi-robot, planning}, pubstate = {published}, tppubtype = {conference} } We propose a distributed control law for a heterogeneous multi-robot coverage problem, where the robots could have different energy characteristics, such as capacity and depletion rates, due to their varying sizes, speeds, capabilities, and payloads. Existing energy-aware coverage control laws consider capacity differences but assume the battery depletion rate to be the same for all robots. In realistic scenarios, however, some robots can consume energy much faster than other robots; for instance, UAVs hover at different altitudes, and these changes could be dynamically updated based on their assigned tasks. Robots' energy capacities and depletion rates need to be considered to maximize the performance of a multi-robot system. To this end, we propose a new energy-aware controller based on Lloyd's algorithm to adapt the weights of the robots based on their energy dynamics and divide the area of interest among the robots accordingly. The controller is theoretically analyzed and extensively evaluated through simulations and real-world demonstrations in multiple realistic scenarios and compared with three baseline control laws to validate its performance and efficacy. |
 | 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2024), 2024. Abstract | Links | BibTeX | Tags: localization, multi-robot, networking @conference{Latif2024c, title = {HGP-RL: Distributed Hierarchical Gaussian Processes for Wi-Fi-based Relative Localization in Multi-Robot Systems }, author = {Ehsan Latif and Ramviyas Parasuraman}, url = {https://ieeexplore.ieee.org/document/10802653}, doi = {10.1109/IROS58592.2024.10802653}, year = {2024}, date = {2024-10-13}, booktitle = {2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2024)}, pages = {3387-3394}, abstract = {Relative localization is crucial for multi-robot systems to perform cooperative tasks, especially in GPS-denied environments. Current techniques for multi-robot relative localization rely on expensive or short-range sensors such as cameras and LIDARs. As a result, these algorithms face challenges such as high computational complexity (e.g., map merging), dependencies on well-structured environments, etc. To remedy this gap, we propose a new distributed approach to perform relative localization (RL) using a common Access Point (AP). To achieve this efficiently, we propose a novel Hierarchical Gaussian Processes (HGP) mapping of the Radio Signal Strength Indicator (RSSI) values from a Wi-Fi AP to which the robots are connected. Each robot performs hierarchical inference using the HGP map to locate the AP in its reference frame, and the robots obtain relative locations of the neighboring robots leveraging AP-oriented algebraic transformations. The approach readily applies to resource-constrained devices and relies only on the ubiquitously-available WiFi RSSI measurement. We extensively validate the performance of the proposed HGR-PL in Robotarium simulations against several state-of-the-art methods. The results indicate superior performance of HGP-RL regarding localization accuracy, computation, and communication overheads. Finally, we showcase the utility of HGP-RL through a multi-robot cooperative experiment to achieve a rendezvous task in a team of three mobile robots.}, keywords = {localization, multi-robot, networking}, pubstate = {published}, tppubtype = {conference} } Relative localization is crucial for multi-robot systems to perform cooperative tasks, especially in GPS-denied environments. Current techniques for multi-robot relative localization rely on expensive or short-range sensors such as cameras and LIDARs. As a result, these algorithms face challenges such as high computational complexity (e.g., map merging), dependencies on well-structured environments, etc. To remedy this gap, we propose a new distributed approach to perform relative localization (RL) using a common Access Point (AP). To achieve this efficiently, we propose a novel Hierarchical Gaussian Processes (HGP) mapping of the Radio Signal Strength Indicator (RSSI) values from a Wi-Fi AP to which the robots are connected. Each robot performs hierarchical inference using the HGP map to locate the AP in its reference frame, and the robots obtain relative locations of the neighboring robots leveraging AP-oriented algebraic transformations. The approach readily applies to resource-constrained devices and relies only on the ubiquitously-available WiFi RSSI measurement. We extensively validate the performance of the proposed HGR-PL in Robotarium simulations against several state-of-the-art methods. The results indicate superior performance of HGP-RL regarding localization accuracy, computation, and communication overheads. Finally, we showcase the utility of HGP-RL through a multi-robot cooperative experiment to achieve a rendezvous task in a team of three mobile robots. |
 | 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2024), 2024. Abstract | Links | BibTeX | Tags: learning, mapping, perception @conference{Ravipati2024, title = {Object-Oriented Material Classification and 3D Clustering for Improved Semantic Perception and Mapping in Mobile Robots}, author = {Siva Krishna Ravipati and Ehsan Latif and Suchendra Bhandarkar and Ramviyas Parasuraman }, url = {https://ieeexplore.ieee.org/document/10801936}, doi = {10.1109/IROS58592.2024.10801936}, year = {2024}, date = {2024-10-13}, booktitle = {2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2024)}, pages = {9729-9736}, abstract = {Classification of different object surface material types can play a significant role in the decision-making algorithms for mobile robots and autonomous vehicles. RGB-based scene-level semantic segmentation has been well-addressed in the literature. However, improving material recognition using the depth modality and its integration with SLAM algorithms for 3D semantic mapping could unlock new potential benefits in the robotics perception pipeline. To this end, we propose a complementarity-aware deep learning approach for RGB-D-based material classification built on top of an object-oriented pipeline. The approach further integrates the ORB-SLAM2 method for 3D scene mapping with multiscale clustering of the detected material semantics in the point cloud map generated by the visual SLAM algorithm. Extensive experimental results with existing public datasets and newly contributed real-world robot datasets demonstrate a significant improvement in material classification and 3D clustering accuracy compared to state-of-the-art approaches for 3D semantic scene mapping. }, keywords = {learning, mapping, perception}, pubstate = {published}, tppubtype = {conference} } Classification of different object surface material types can play a significant role in the decision-making algorithms for mobile robots and autonomous vehicles. RGB-based scene-level semantic segmentation has been well-addressed in the literature. However, improving material recognition using the depth modality and its integration with SLAM algorithms for 3D semantic mapping could unlock new potential benefits in the robotics perception pipeline. To this end, we propose a complementarity-aware deep learning approach for RGB-D-based material classification built on top of an object-oriented pipeline. The approach further integrates the ORB-SLAM2 method for 3D scene mapping with multiscale clustering of the detected material semantics in the point cloud map generated by the visual SLAM algorithm. Extensive experimental results with existing public datasets and newly contributed real-world robot datasets demonstrate a significant improvement in material classification and 3D clustering accuracy compared to state-of-the-art approaches for 3D semantic scene mapping. |
 | Anchor-Oriented Localized Voronoi Partitioning for GPS-denied Multi-Robot Coverage Conference 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2024), 2024. Abstract | Links | BibTeX | Tags: cooperation, localization, multi-robot, planning @conference{Munir2024, title = {Anchor-Oriented Localized Voronoi Partitioning for GPS-denied Multi-Robot Coverage}, author = {Aiman Munir and Ehsan Latif and Ramviyas Parasuraman}, url = {https://ieeexplore.ieee.org/document/10802222}, doi = {10.1109/IROS58592.2024.10802222}, year = {2024}, date = {2024-10-13}, booktitle = {2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2024)}, pages = {3395-3402}, abstract = {Multi-robot coverage is crucial in numerous applications, including environmental monitoring, search and rescue operations, and precision agriculture. In modern applications, a multi-robot team must collaboratively explore unknown spatial fields in GPS-denied and extreme environments where global localization is unavailable. Coverage algorithms typically assume that the robot positions and the coverage environment are defined in a global reference frame. However, coordinating robot motion and ensuring coverage of the shared convex workspace without global localization is challenging. This paper proposes a novel anchor-oriented coverage (AOC) approach to generate dynamic localized Voronoi partitions based around a common anchor position. We further propose a consensus-based coordination algorithm that achieves agreement on the coverage workspace around the anchor in the robots' relative frames of reference. Through extensive simulations and real-world experiments, we demonstrate that the proposed anchor-oriented approach using localized Voronoi partitioning performs as well as the state-of-the-art coverage controller using GPS. }, keywords = {cooperation, localization, multi-robot, planning}, pubstate = {published}, tppubtype = {conference} } Multi-robot coverage is crucial in numerous applications, including environmental monitoring, search and rescue operations, and precision agriculture. In modern applications, a multi-robot team must collaboratively explore unknown spatial fields in GPS-denied and extreme environments where global localization is unavailable. Coverage algorithms typically assume that the robot positions and the coverage environment are defined in a global reference frame. However, coordinating robot motion and ensuring coverage of the shared convex workspace without global localization is challenging. This paper proposes a novel anchor-oriented coverage (AOC) approach to generate dynamic localized Voronoi partitions based around a common anchor position. We further propose a consensus-based coordination algorithm that achieves agreement on the coverage workspace around the anchor in the robots' relative frames of reference. Through extensive simulations and real-world experiments, we demonstrate that the proposed anchor-oriented approach using localized Voronoi partitioning performs as well as the state-of-the-art coverage controller using GPS. |
 | Route Planning for Electric Vehicles with Charging Constraints Conference 2024 IEEE 100th Vehicular Technology Conference (VTC2024-Fall), 2024. Abstract | Links | BibTeX | Tags: control, learning, multi-robot systems @conference{Munir2024c, title = {Route Planning for Electric Vehicles with Charging Constraints}, author = {Aiman Munir, Ramviyas Parasuraman, Jin Ye, WenZhan Song}, url = {https://ieeexplore.ieee.org/abstract/document/10757558}, doi = {10.1109/VTC2024-Fall63153.2024.10757558}, year = {2024}, date = {2024-10-10}, booktitle = {2024 IEEE 100th Vehicular Technology Conference (VTC2024-Fall)}, pages = {2577-2465}, abstract = {Recent studies demonstrate the efficacy of machine learning algorithms for learning strategies to solve combinatorial optimization problems. This study presents a novel solution to address the Electric Vehicle Routing Problem with Time Windows (EVRPTW), leveraging deep reinforcement learning (DRL) techniques. Existing DRL approaches frequently encounter challenges when addressing the EVRPTW problem: RNN-based decoders struggle with capturing long-term dependencies, while DDQN models exhibit limited generalization across various problem sizes. To overcome these limitations, we introduce a transformer-based model with a heterogeneous attention mechanism. Transformers excel at capturing long-term dependencies and demonstrate superior generalization across diverse problem instances. We validate the efficacy of our proposed approach through comparative analysis against two state-of-the-art solutions for EVRPTW. The results demonstrated the efficacy of the proposed model in minimizing the distance traveled and robust generalization across varying problem sizes. }, keywords = {control, learning, multi-robot systems}, pubstate = {published}, tppubtype = {conference} } Recent studies demonstrate the efficacy of machine learning algorithms for learning strategies to solve combinatorial optimization problems. This study presents a novel solution to address the Electric Vehicle Routing Problem with Time Windows (EVRPTW), leveraging deep reinforcement learning (DRL) techniques. Existing DRL approaches frequently encounter challenges when addressing the EVRPTW problem: RNN-based decoders struggle with capturing long-term dependencies, while DDQN models exhibit limited generalization across various problem sizes. To overcome these limitations, we introduce a transformer-based model with a heterogeneous attention mechanism. Transformers excel at capturing long-term dependencies and demonstrate superior generalization across diverse problem instances. We validate the efficacy of our proposed approach through comparative analysis against two state-of-the-art solutions for EVRPTW. The results demonstrated the efficacy of the proposed model in minimizing the distance traveled and robust generalization across varying problem sizes. |
 | Communication-Aware Consistent Edge Selection for Mobile Users and Autonomous Vehicles Conference 2024 IEEE 100th Vehicular Technology Conference (VTC2024-Fall), 2024. Abstract | Links | BibTeX | Tags: computing, multi-robot systems, networking @conference{Tahir2024, title = {Communication-Aware Consistent Edge Selection for Mobile Users and Autonomous Vehicles}, author = {Nazish Tahir, Ramviyas Parasuraman, Haijian Sun}, url = {https://ieeexplore.ieee.org/abstract/document/10757784}, doi = {10.1109/VTC2024-Fall63153.2024.10757784}, year = {2024}, date = {2024-10-10}, booktitle = {2024 IEEE 100th Vehicular Technology Conference (VTC2024-Fall)}, pages = {2577-2465}, abstract = {Offloading time-sensitive, computationally intensive tasks-such as advanced learning algorithms for autonomous driving-from vehicles to nearby edge servers, vehicle-to-infrastructure (V2I) systems, or other collaborating vehicles via vehicle-to-vehicle (V2V) communication enhances service efficiency. However, whence traversing the path to the destination, the vehicle's mobility necessitates frequent handovers among the access points (APs) to maintain continuous and uninterrupted wireless connections to maintain the network's Quality of Service (QoS). These frequent handovers subsequently lead to task migrations among the edge servers associated with the respective APs. This paper addresses the joint problem of task migration and access-point handover by proposing a deep reinforcement learning framework based on the Deep Deterministic Policy Gradient (DDPG) algorithm. A joint allocation method of communication and computation of APs is proposed to minimize computational load, service latency, and interruptions with the overarching goal of maximizing QoS. We implement and evaluate our proposed framework on simulated experiments to achieve smooth and seamless task switching among edge servers, ultimately reducing latency. }, keywords = {computing, multi-robot systems, networking}, pubstate = {published}, tppubtype = {conference} } Offloading time-sensitive, computationally intensive tasks-such as advanced learning algorithms for autonomous driving-from vehicles to nearby edge servers, vehicle-to-infrastructure (V2I) systems, or other collaborating vehicles via vehicle-to-vehicle (V2V) communication enhances service efficiency. However, whence traversing the path to the destination, the vehicle's mobility necessitates frequent handovers among the access points (APs) to maintain continuous and uninterrupted wireless connections to maintain the network's Quality of Service (QoS). These frequent handovers subsequently lead to task migrations among the edge servers associated with the respective APs. This paper addresses the joint problem of task migration and access-point handover by proposing a deep reinforcement learning framework based on the Deep Deterministic Policy Gradient (DDPG) algorithm. A joint allocation method of communication and computation of APs is proposed to minimize computational load, service latency, and interruptions with the overarching goal of maximizing QoS. We implement and evaluate our proposed framework on simulated experiments to achieve smooth and seamless task switching among edge servers, ultimately reducing latency. |
 | PhysicsAssistant: An LLM-Powered Interactive Learning Robot for Physics Lab Investigations Conference The 33rd IEEE International Conference on Robot and Human Interactive Communication, IEEE RO-MAN 2024, 2024. Abstract | Links | BibTeX | Tags: assistive devices, human-robot interaction, human-robot interface @conference{Latif2024bb, title = {PhysicsAssistant: An LLM-Powered Interactive Learning Robot for Physics Lab Investigations}, author = {Ehsan Latif and Ramviyas Parasuraman and Xiaoming Zhai}, doi = {10.1109/RO-MAN60168.2024.10731312}, year = {2024}, date = {2024-08-30}, booktitle = {The 33rd IEEE International Conference on Robot and Human Interactive Communication, IEEE RO-MAN 2024}, abstract = { Robot systems in education can leverage Large language models' (LLMs) natural language understanding capabilities to provide assistance and facilitate learning. This paper proposes a multimodal interactive robot (PhysicsAssistant) built on YOLOv8 object detection, cameras, speech recognition, and chatbot using LLM to provide assistance to students' physics labs. We conduct a user study on ten 8th-grade students to empirically evaluate the performance of PhysicsAssistant with a human expert. The Expert rates the assistants' responses to student queries on a 0-4 scale based on Bloom's taxonomy to provide educational support. We have compared the performance of PhysicsAssistant (YOLOv8+GPT-3.5-turbo) with GPT-4 and found that the human expert rating of both systems for factual understanding is same. However, the rating of GPT-4 for conceptual and procedural knowledge (3 and 3.2 vs 2.2 and 2.6, respectively) is significantly higher than PhysicsAssistant (p $<$ 0.05). However, the response time of GPT-4 is significantly higher than PhysicsAssistant (3.54 vs 1.64 sec, p $<$ 0.05). Hence, despite the relatively lower response quality of PhysicsAssistant than GPT-4, it has shown potential for being used as a real-time lab assistant to provide timely responses and can offload teachers' labor to assist with repetitive tasks. To the best of our knowledge, this is the first attempt to build such an interactive multimodal robotic assistant for K-12 science (physics) education. }, keywords = {assistive devices, human-robot interaction, human-robot interface}, pubstate = {published}, tppubtype = {conference} } Robot systems in education can leverage Large language models' (LLMs) natural language understanding capabilities to provide assistance and facilitate learning. This paper proposes a multimodal interactive robot (PhysicsAssistant) built on YOLOv8 object detection, cameras, speech recognition, and chatbot using LLM to provide assistance to students' physics labs. We conduct a user study on ten 8th-grade students to empirically evaluate the performance of PhysicsAssistant with a human expert. The Expert rates the assistants' responses to student queries on a 0-4 scale based on Bloom's taxonomy to provide educational support. We have compared the performance of PhysicsAssistant (YOLOv8+GPT-3.5-turbo) with GPT-4 and found that the human expert rating of both systems for factual understanding is same. However, the rating of GPT-4 for conceptual and procedural knowledge (3 and 3.2 vs 2.2 and 2.6, respectively) is significantly higher than PhysicsAssistant (p $<$ 0.05). However, the response time of GPT-4 is significantly higher than PhysicsAssistant (3.54 vs 1.64 sec, p $<$ 0.05). Hence, despite the relatively lower response quality of PhysicsAssistant than GPT-4, it has shown potential for being used as a real-time lab assistant to provide timely responses and can offload teachers' labor to assist with repetitive tasks. To the best of our knowledge, this is the first attempt to build such an interactive multimodal robotic assistant for K-12 science (physics) education. |
 | Map2Schedule: An End-to-End Link Scheduling Method for Urban V2V Communications Conference 2024 IEEE International Conference on Communications (ICC), 2024, (Accepted for Presentation at ICC 2024). Abstract | Links | BibTeX | Tags: multi-robot, networking @conference{Zhang2024, title = {Map2Schedule: An End-to-End Link Scheduling Method for Urban V2V Communications}, author = {Lihao Zhang, Haijian Sun, Jin Sun, Ramviyas Parasuraman, Yinghui Ye, Rose Qingyang Hu}, url = {https://ieeexplore.ieee.org/document/10622509}, doi = {10.1109/ICC51166.2024.10622509}, year = {2024}, date = {2024-06-13}, booktitle = {2024 IEEE International Conference on Communications (ICC)}, abstract = {Urban vehicle-to-vehicle (V2V) link scheduling with shared spectrum is a challenging problem. Its main goal is to find the scheduling policy that can maximize system performance (usually the sum capacity of each link or their energy efficiency). Given that each link can experience interference from all other active links, the scheduling becomes a combinatorial integer programming problem and generally does not scale well with the number of V2V pairs. Moreover, link scheduling requires accurate channel state information (CSI), which is very difficult to estimate with good accuracy under high vehicle mobility. In this paper, we propose an end-to-end urban V2V link scheduling method called Map2Schedule, which can directly generate V2V scheduling policy from the city map and vehicle locations. Map2Schedule delivers comparable performance to the physical-model-based methods in urban settings while maintaining low computation complexity. This enhanced performance is achieved by machine learning (ML) technologies. Specifically, we first deploy the convolutional neural network (CNN) model to estimate the CSI from street layout and vehicle locations and then apply the graph embedding model for optimal scheduling policy. The results show that the proposed method can achieve high accuracy with much lower overhead and latency.}, note = {Accepted for Presentation at ICC 2024}, keywords = {multi-robot, networking}, pubstate = {published}, tppubtype = {conference} } Urban vehicle-to-vehicle (V2V) link scheduling with shared spectrum is a challenging problem. Its main goal is to find the scheduling policy that can maximize system performance (usually the sum capacity of each link or their energy efficiency). Given that each link can experience interference from all other active links, the scheduling becomes a combinatorial integer programming problem and generally does not scale well with the number of V2V pairs. Moreover, link scheduling requires accurate channel state information (CSI), which is very difficult to estimate with good accuracy under high vehicle mobility. In this paper, we propose an end-to-end urban V2V link scheduling method called Map2Schedule, which can directly generate V2V scheduling policy from the city map and vehicle locations. Map2Schedule delivers comparable performance to the physical-model-based methods in urban settings while maintaining low computation complexity. This enhanced performance is achieved by machine learning (ML) technologies. Specifically, we first deploy the convolutional neural network (CNN) model to estimate the CSI from street layout and vehicle locations and then apply the graph embedding model for optimal scheduling policy. The results show that the proposed method can achieve high accuracy with much lower overhead and latency. |
 | Bayesian Soft Actor-Critic: A Directed Acyclic Strategy Graph Based Deep Reinforcement Learning Conference 2024 ACM/SIGAPP Symposium on Applied Computing (SAC) , IRMAS Track 2024. Abstract | Links | BibTeX | Tags: control, learning @conference{Yang2024, title = {Bayesian Soft Actor-Critic: A Directed Acyclic Strategy Graph Based Deep Reinforcement Learning}, author = {Qin Yang and Ramviyas Parasuraman}, url = {https://dl.acm.org/doi/10.1145/3605098.3636113}, doi = {10.1145/3605098.3636113}, year = {2024}, date = {2024-04-08}, booktitle = {2024 ACM/SIGAPP Symposium on Applied Computing (SAC) }, series = {IRMAS Track}, abstract = {Adopting reasonable strategies is challenging but crucial for an intelligent agent with limited resources working in hazardous, unstructured, and dynamic environments to improve the system's utility, decrease the overall cost, and increase mission success probability. This paper proposes a novel directed acyclic strategy graph decomposition approach based on Bayesian chaining to separate an intricate policy into several simple sub-policies and organize their relationships as Bayesian strategy networks (BSN). We integrate this approach into the state-of-the-art DRL method -- soft actor-critic (SAC), and build the corresponding Bayesian soft actor-critic (BSAC) model by organizing several sub-policies as a joint policy. We compare our method against the state-of-the-art deep reinforcement learning algorithms on the standard continuous control benchmarks in the OpenAI Gym environment. The results demonstrate that the promising potential of the BSAC method significantly improves training efficiency. }, keywords = {control, learning}, pubstate = {published}, tppubtype = {conference} } Adopting reasonable strategies is challenging but crucial for an intelligent agent with limited resources working in hazardous, unstructured, and dynamic environments to improve the system's utility, decrease the overall cost, and increase mission success probability. This paper proposes a novel directed acyclic strategy graph decomposition approach based on Bayesian chaining to separate an intricate policy into several simple sub-policies and organize their relationships as Bayesian strategy networks (BSN). We integrate this approach into the state-of-the-art DRL method -- soft actor-critic (SAC), and build the corresponding Bayesian soft actor-critic (BSAC) model by organizing several sub-policies as a joint policy. We compare our method against the state-of-the-art deep reinforcement learning algorithms on the standard continuous control benchmarks in the OpenAI Gym environment. The results demonstrate that the promising potential of the BSAC method significantly improves training efficiency. |
 | Anchor-oriented Multi-Robot Coverage without Global Localization Workshop IEEE ICRA 2024 Workshop on Sensing and Perception in Extreme Environments (HERMES), 2024, (Spotlight Presentation). Abstract | Links | BibTeX | Tags: localization, multi-robot systems, planning @workshop{Munir2024d, title = {Anchor-oriented Multi-Robot Coverage without Global Localization}, author = {Aiman Munir, Ehsan Latif, and Ramviyas Parasuraman}, url = {https://hermes-workshop.com/2024.html}, year = {2024}, date = {2024-05-13}, booktitle = {IEEE ICRA 2024 Workshop on Sensing and Perception in Extreme Environments (HERMES)}, abstract = {Multi-robot coverage is crucial in numerous applications, including environmental monitoring, search and rescue operations, and precision agriculture. In modern applications, a multi-robot team must explore unknown spatial fields collaboratively in GPS-denied environments without compromising their location privacy. Coverage algorithms typically assume that the robot positions and the coverage environment are defined in a global reference frame. However, coordinating robot motion and ensuring coverage of the shared convex workspace without global localization is challenging. This paper proposes a novel anchor-oriented coverage (AOC) approach to generate dynamic Voronoi partitions with only relative position measurements. We further propose a consensus-based coordination algorithm that achieves agreement on the coverage workspace around the anchor in the robot's relative frames of reference. Through extensive simulations and real-world experiments, we demonstrate that the performance of the proposed anchor-oriented approach using relative localization matches with the state-of-the-art coverage controller with global localization.}, note = {Spotlight Presentation}, keywords = {localization, multi-robot systems, planning}, pubstate = {published}, tppubtype = {workshop} } Multi-robot coverage is crucial in numerous applications, including environmental monitoring, search and rescue operations, and precision agriculture. In modern applications, a multi-robot team must explore unknown spatial fields collaboratively in GPS-denied environments without compromising their location privacy. Coverage algorithms typically assume that the robot positions and the coverage environment are defined in a global reference frame. However, coordinating robot motion and ensuring coverage of the shared convex workspace without global localization is challenging. This paper proposes a novel anchor-oriented coverage (AOC) approach to generate dynamic Voronoi partitions with only relative position measurements. We further propose a consensus-based coordination algorithm that achieves agreement on the coverage workspace around the anchor in the robot's relative frames of reference. Through extensive simulations and real-world experiments, we demonstrate that the performance of the proposed anchor-oriented approach using relative localization matches with the state-of-the-art coverage controller with global localization. |
 | PhysicsAssistant: An LLM-Powered Interactive Learning Robot for Physics Lab Investigations Workshop IEEE ICRA 2024 Workshop on Accelerating Discovery in Natural Science Laboratories with AI and Robotics, 2024, (Selected for the Pioneer Award). Abstract | Links | BibTeX | Tags: assistive devices, autonomy, human-robot interaction, human-robot interface, learning @workshop{Latif2024d, title = {PhysicsAssistant: An LLM-Powered Interactive Learning Robot for Physics Lab Investigations}, author = {Ehsan Latif, Ramviyas Parasuraman, and Xiaoming Zhai}, url = {https://sites.google.com/view/icra24-accelerating-discovery}, year = {2024}, date = {2024-05-13}, booktitle = {IEEE ICRA 2024 Workshop on Accelerating Discovery in Natural Science Laboratories with AI and Robotics}, abstract = {Robot systems in education can leverage Large language models' (LLMs) natural language understanding capabilities to provide assistance and facilitate learning. This paper proposes a multimodal interactive robot (PhysicsAssistant) built on YOLOv8 object detection, cameras, speech recognition, and chatbot using LLM to provide assistance to students' physics labs. We conduct a user study on ten 8th-grade students to empirically evaluate the performance of PhysicsAssistant with a human expert. The Expert rates the assistants' responses to student queries on a 0-4 scale based on Bloom's taxonomy to provide educational support. We have compared the performance of PhysicsAssistant (YOLOv8+GPT-3.5-turbo) with GPT-4 and found that the human expert rating of both systems for factual understanding is same. However, the rating of GPT-4 for conceptual and procedural knowledge (3 and 3.2 vs 2.2 and 2.6, respectively) is significantly higher than PhysicsAssistant (p $<$ 0.05). However, the response time of GPT-4 is significantly higher than PhysicsAssistant (3.54 vs 1.64 sec, p $<$ 0.05). Hence, despite the relatively lower response quality of PhysicsAssistant than GPT-4, it has shown potential for being used as a real-time lab assistant to provide timely responses and can offload teachers' labor to assist with repetitive tasks. To the best of our knowledge, this is the first attempt to build such an interactive multimodal robotic assistant for K-12 science (physics) education. }, note = {Selected for the Pioneer Award}, keywords = {assistive devices, autonomy, human-robot interaction, human-robot interface, learning}, pubstate = {published}, tppubtype = {workshop} } Robot systems in education can leverage Large language models' (LLMs) natural language understanding capabilities to provide assistance and facilitate learning. This paper proposes a multimodal interactive robot (PhysicsAssistant) built on YOLOv8 object detection, cameras, speech recognition, and chatbot using LLM to provide assistance to students' physics labs. We conduct a user study on ten 8th-grade students to empirically evaluate the performance of PhysicsAssistant with a human expert. The Expert rates the assistants' responses to student queries on a 0-4 scale based on Bloom's taxonomy to provide educational support. We have compared the performance of PhysicsAssistant (YOLOv8+GPT-3.5-turbo) with GPT-4 and found that the human expert rating of both systems for factual understanding is same. However, the rating of GPT-4 for conceptual and procedural knowledge (3 and 3.2 vs 2.2 and 2.6, respectively) is significantly higher than PhysicsAssistant (p $<$ 0.05). However, the response time of GPT-4 is significantly higher than PhysicsAssistant (3.54 vs 1.64 sec, p $<$ 0.05). Hence, despite the relatively lower response quality of PhysicsAssistant than GPT-4, it has shown potential for being used as a real-time lab assistant to provide timely responses and can offload teachers' labor to assist with repetitive tasks. To the best of our knowledge, this is the first attempt to build such an interactive multimodal robotic assistant for K-12 science (physics) education. |
2023 |
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 | Exploration–Exploitation Tradeoff in the Adaptive Information Sampling of Unknown Spatial Fields with Mobile Robots Journal Article Sensors, 23 (23), 2023. Abstract | Links | BibTeX | Tags: control, mapping, multi-robot, planning @article{Munir2022b, title = {Exploration–Exploitation Tradeoff in the Adaptive Information Sampling of Unknown Spatial Fields with Mobile Robots}, author = {Aiman Munir and Ramviyas Parasuraman}, url = {https://www.mdpi.com/1424-8220/23/23/9600}, doi = {10.3390/s23239600}, year = {2023}, date = {2023-12-04}, journal = {Sensors}, volume = {23}, number = {23}, abstract = {Adaptive information-sampling approaches enable efficient selection of mobile robots’ waypoints through which the accurate sensing and mapping of a physical process, such as the radiation or field intensity, can be obtained. A key parameter in the informative sampling objective function could be optimized balance the need to explore new information where the uncertainty is very high and to exploit the data sampled so far, with which a great deal of the underlying spatial fields can be obtained, such as the source locations or modalities of the physical process. However, works in the literature have either assumed the robot’s energy is unconstrained or used a homogeneous availability of energy capacity among different robots. Therefore, this paper analyzes the impact of the adaptive information-sampling algorithm’s information function used in exploration and exploitation to achieve a tradeoff between balancing the mapping, localization, and energy efficiency objectives. We use Gaussian process regression (GPR) to predict and estimate confidence bounds, thereby determining each point’s informativeness. Through extensive experimental data, we provide a deeper and holistic perspective on the effect of information function parameters on the prediction map’s accuracy (RMSE), confidence bound (variance), energy consumption (distance), and time spent (sample count) in both single- and multi-robot scenarios. The results provide meaningful insights into choosing the appropriate energy-aware information function parameters based on sensing objectives (e.g., source localization or mapping). Based on our analysis, we can conclude that it would be detrimental to give importance only to the uncertainty of the information function (which would explode the energy needs) or to the predictive mean of the information (which would jeopardize the mapping accuracy). By assigning more importance to the information uncertainly with some non-zero importance to the information value (e.g., 75:25 ratio), it is possible to achieve an optimal tradeoff between exploration and exploitation objectives while keeping the energy requirements manageable.}, keywords = {control, mapping, multi-robot, planning}, pubstate = {published}, tppubtype = {article} } Adaptive information-sampling approaches enable efficient selection of mobile robots’ waypoints through which the accurate sensing and mapping of a physical process, such as the radiation or field intensity, can be obtained. A key parameter in the informative sampling objective function could be optimized balance the need to explore new information where the uncertainty is very high and to exploit the data sampled so far, with which a great deal of the underlying spatial fields can be obtained, such as the source locations or modalities of the physical process. However, works in the literature have either assumed the robot’s energy is unconstrained or used a homogeneous availability of energy capacity among different robots. Therefore, this paper analyzes the impact of the adaptive information-sampling algorithm’s information function used in exploration and exploitation to achieve a tradeoff between balancing the mapping, localization, and energy efficiency objectives. We use Gaussian process regression (GPR) to predict and estimate confidence bounds, thereby determining each point’s informativeness. Through extensive experimental data, we provide a deeper and holistic perspective on the effect of information function parameters on the prediction map’s accuracy (RMSE), confidence bound (variance), energy consumption (distance), and time spent (sample count) in both single- and multi-robot scenarios. The results provide meaningful insights into choosing the appropriate energy-aware information function parameters based on sensing objectives (e.g., source localization or mapping). Based on our analysis, we can conclude that it would be detrimental to give importance only to the uncertainty of the information function (which would explode the energy needs) or to the predictive mean of the information (which would jeopardize the mapping accuracy). By assigning more importance to the information uncertainly with some non-zero importance to the information value (e.g., 75:25 ratio), it is possible to achieve an optimal tradeoff between exploration and exploitation objectives while keeping the energy requirements manageable. |
 | On the Intersection of Computational Geometry Algorithms with Mobile Robot Path Planning Journal Article Algorithms, 16 (11), pp. 498, 2023. Abstract | Links | BibTeX | Tags: planning @article{Latif2023e, title = {On the Intersection of Computational Geometry Algorithms with Mobile Robot Path Planning}, author = {Ehsan Latif and Ramviyas Parasuraman}, url = {https://www.mdpi.com/1999-4893/16/11/498}, doi = {10.3390/a16110498}, year = {2023}, date = {2023-10-27}, journal = {Algorithms}, volume = {16}, number = {11}, pages = {498}, abstract = {In the mathematical discipline of computational geometry (CG), practical algorithms for resolving geometric input and output issues are designed, analyzed, and put into practice. It is sometimes used to refer to pattern recognition and to define the solid modeling methods for manipulating curves and surfaces. CG is a rich field encompassing theories to solve complex optimization problems, such as path planning for mobile robot systems and extension to distributed multi-robot systems. This brief review discusses the fundamentals of CG and its application in solving well-known automated path-planning problems in single- and multi-robot systems. We also discuss three winning algorithms from the CG-SHOP (Computational Geometry: Solving Hard Optimization Problems) 2021 competition to evidence the practicality of CG in multi-robotic systems. We also mention some open problems at the intersection of CG and robotics. This review provides insights into the potential use of CG in robotics and future research directions at their intersection.}, keywords = {planning}, pubstate = {published}, tppubtype = {article} } In the mathematical discipline of computational geometry (CG), practical algorithms for resolving geometric input and output issues are designed, analyzed, and put into practice. It is sometimes used to refer to pattern recognition and to define the solid modeling methods for manipulating curves and surfaces. CG is a rich field encompassing theories to solve complex optimization problems, such as path planning for mobile robot systems and extension to distributed multi-robot systems. This brief review discusses the fundamentals of CG and its application in solving well-known automated path-planning problems in single- and multi-robot systems. We also discuss three winning algorithms from the CG-SHOP (Computational Geometry: Solving Hard Optimization Problems) 2021 competition to evidence the practicality of CG in multi-robotic systems. We also mention some open problems at the intersection of CG and robotics. This review provides insights into the potential use of CG in robotics and future research directions at their intersection. |
 | KT-BT: A Framework for Knowledge Transfer Through Behavior Trees in Multi-Robot Systems Journal Article IEEE Transactions on Robotics, 30 (5), pp. 4114 - 4130, 2023. Abstract | Links | BibTeX | Tags: autonomy, behavior-trees, heterogeneity, multi-robot, planning @article{Venkata2023b, title = {KT-BT: A Framework for Knowledge Transfer Through Behavior Trees in Multi-Robot Systems}, author = {Sanjay Sarma Oruganti Venkata, Ramviyas Parasuraman, Ramana Pidaparti}, url = {https://ieeexplore.ieee.org/abstract/document/10183654}, doi = {10.1109/TRO.2023.3290449}, year = {2023}, date = {2023-07-13}, journal = {IEEE Transactions on Robotics}, volume = {30}, number = {5}, pages = {4114 - 4130}, abstract = {Multi-Robot and Multi-Agent Systems demonstrate collective (swarm) intelligence through systematic and distributed integration of local behaviors in a group. Agents sharing knowledge about the mission and environment can enhance performance at individual and mission levels. However, this is difficult to achieve, partly due to the lack of a generic framework for transferring part of the known knowledge (behaviors) between agents. This paper presents a new knowledge representation framework and a transfer strategy called KT-BT: Knowledge Transfer through Behavior Trees. The KT-BT framework follows a query-response-update mechanism through an online Behavior Tree framework, where agents broadcast queries for unknown conditions and respond with appropriate knowledge using a condition-action-control sub-flow. We embed a novel grammar structure called stringBT that encodes knowledge, enabling behavior sharing. We theoretically investigate the properties of the KT-BT framework in achieving homogeneity of high knowledge across the entire group compared to a heterogeneous system without the capability of sharing their knowledge. We extensively verify our framework in a simulated multi-robot search and rescue problem. The results show successful knowledge transfers and improved group performance in various scenarios. We further study the effects of opportunities and communication range on group performance, knowledge spread, and functional heterogeneity in a group of agents, presenting interesting insights.}, keywords = {autonomy, behavior-trees, heterogeneity, multi-robot, planning}, pubstate = {published}, tppubtype = {article} } Multi-Robot and Multi-Agent Systems demonstrate collective (swarm) intelligence through systematic and distributed integration of local behaviors in a group. Agents sharing knowledge about the mission and environment can enhance performance at individual and mission levels. However, this is difficult to achieve, partly due to the lack of a generic framework for transferring part of the known knowledge (behaviors) between agents. This paper presents a new knowledge representation framework and a transfer strategy called KT-BT: Knowledge Transfer through Behavior Trees. The KT-BT framework follows a query-response-update mechanism through an online Behavior Tree framework, where agents broadcast queries for unknown conditions and respond with appropriate knowledge using a condition-action-control sub-flow. We embed a novel grammar structure called stringBT that encodes knowledge, enabling behavior sharing. We theoretically investigate the properties of the KT-BT framework in achieving homogeneity of high knowledge across the entire group compared to a heterogeneous system without the capability of sharing their knowledge. We extensively verify our framework in a simulated multi-robot search and rescue problem. The results show successful knowledge transfers and improved group performance in various scenarios. We further study the effects of opportunities and communication range on group performance, knowledge spread, and functional heterogeneity in a group of agents, presenting interesting insights. |
 | Rapid prediction of network quality in mobile robots Journal Article Ad Hoc Networks, 138 , 2023, ISSN: 1570-8705. Abstract | Links | BibTeX | Tags: networking, planning @article{Parasuraman2023, title = {Rapid prediction of network quality in mobile robots}, author = {Ramviyas Parasuraman and Byung-Cheol Min and Petter Ögren}, doi = {10.1016/j.adhoc.2022.103014}, issn = {1570-8705}, year = {2023}, date = {2023-01-01}, journal = {Ad Hoc Networks}, volume = {138}, abstract = {Mobile robots rely on wireless networks for sharing sensor data from remote missions. The robot’s spatial network quality will vary considerably across a given mission environment and network access point (AP) location, which are often unknown apriori. Therefore, predicting these spatial variations becomes essential and challenging, especially in dynamic and unstructured environments. To address this challenge, we propose an online algorithm to predict wireless connection quality measured through the well-exploited Radio Signal Strength (RSS) metric in the future positions along a mobile robot’s trajectory. We assume no knowledge of the environment or AP positions other than robot odometry and RSS measurements at the previous trajectory points. We propose a discrete Kalman filter-based solution considering path loss and shadowing effects. The algorithm is evaluated with unique real-world datasets in indoor, outdoor, and underground data showing prediction accuracy of up to 96%, revealing significant performance improvements over conventional approaches, including Gaussian Processes Regression. Having such accurate predictions will help the robot plan its trajectory and task operations in a communication-aware manner ensuring mission success. Further, we extensively analyze the approach regarding the impacts of localization error, source location, mobility, antenna type, and connection failures on prediction accuracy, providing novel perspectives and observations for performance evaluation.}, keywords = {networking, planning}, pubstate = {published}, tppubtype = {article} } Mobile robots rely on wireless networks for sharing sensor data from remote missions. The robot’s spatial network quality will vary considerably across a given mission environment and network access point (AP) location, which are often unknown apriori. Therefore, predicting these spatial variations becomes essential and challenging, especially in dynamic and unstructured environments. To address this challenge, we propose an online algorithm to predict wireless connection quality measured through the well-exploited Radio Signal Strength (RSS) metric in the future positions along a mobile robot’s trajectory. We assume no knowledge of the environment or AP positions other than robot odometry and RSS measurements at the previous trajectory points. We propose a discrete Kalman filter-based solution considering path loss and shadowing effects. The algorithm is evaluated with unique real-world datasets in indoor, outdoor, and underground data showing prediction accuracy of up to 96%, revealing significant performance improvements over conventional approaches, including Gaussian Processes Regression. Having such accurate predictions will help the robot plan its trajectory and task operations in a communication-aware manner ensuring mission success. Further, we extensively analyze the approach regarding the impacts of localization error, source location, mobility, antenna type, and connection failures on prediction accuracy, providing novel perspectives and observations for performance evaluation. |
 | Consensus-based Resource Scheduling for Collaborative Multi-Robot Tasks Conference 2023 Sixth IEEE International Conference on Robotic Computing (IRC), 2023. Abstract | Links | BibTeX | Tags: computing, multi-robot, networking @conference{Tahir2023b, title = {Consensus-based Resource Scheduling for Collaborative Multi-Robot Tasks}, author = {Nazish Tahir and Ramviyas Parasuraman}, url = {https://ieeexplore.ieee.org/document/10473544}, doi = {10.1109/IRC59093.2023.00059}, year = {2023}, date = {2023-12-13}, booktitle = {2023 Sixth IEEE International Conference on Robotic Computing (IRC)}, abstract = {We propose integrating the edge-computing paradigm into the multi-robot collaborative scheduling to maximize resource utilization for complex collaborative tasks, which many robots must perform together. Examples include collaborative map-merging to produce a live global map during exploration instead of traditional approaches that schedule tasks on centralized cloud-based systems to facilitate computing. Our decentralized approach to a consensus-based scheduling strategy benefits a multi-robot-edge collaboration system by adapting to dynamic computation needs and communication-changing statistics as the system tries to optimize resources while maintaining overall performance objectives. Before collaborative task offloading, continuous device, and network profiling are performed at the computing resources, and the distributed scheduling scheme then selects the resource with maximum utility derived using a utility maximization approach. Thorough evaluations with and without edge servers on simulation and real-world multi-robot systems demonstrate that a lower task latency, a large throughput gain, and better frame rate processing may be achieved compared to the conventional edge-based systems.}, keywords = {computing, multi-robot, networking}, pubstate = {published}, tppubtype = {conference} } We propose integrating the edge-computing paradigm into the multi-robot collaborative scheduling to maximize resource utilization for complex collaborative tasks, which many robots must perform together. Examples include collaborative map-merging to produce a live global map during exploration instead of traditional approaches that schedule tasks on centralized cloud-based systems to facilitate computing. Our decentralized approach to a consensus-based scheduling strategy benefits a multi-robot-edge collaboration system by adapting to dynamic computation needs and communication-changing statistics as the system tries to optimize resources while maintaining overall performance objectives. Before collaborative task offloading, continuous device, and network profiling are performed at the computing resources, and the distributed scheduling scheme then selects the resource with maximum utility derived using a utility maximization approach. Thorough evaluations with and without edge servers on simulation and real-world multi-robot systems demonstrate that a lower task latency, a large throughput gain, and better frame rate processing may be achieved compared to the conventional edge-based systems. |
 | Utility AI for Dynamic Task Offloading in the Multi-Edge Infrastructure Conference 2023 Sixth IEEE International Conference on Robotic Computing (IRC), 2023. Abstract | Links | BibTeX | Tags: computing, multi-robot, networking @conference{Tahir2023c, title = {Utility AI for Dynamic Task Offloading in the Multi-Edge Infrastructure}, author = {Nazish Tahir and Ramviyas Parasuraman}, url = {https://ieeexplore.ieee.org/document/10473568}, doi = {10.1109/IRC59093.2023.00060}, year = {2023}, date = {2023-12-13}, booktitle = {2023 Sixth IEEE International Conference on Robotic Computing (IRC)}, abstract = {To circumvent persistent connectivity to the cloud infrastructure, the current emphasis on computing at network edge devices in the multi-robot domain is a promising enabler for delay-sensitive jobs, yet its adoption is rife with challenges. This paper proposes a novel utility-aware dynamic task offloading strategy based on a multi-edge-robot system that takes into account computation, communication, and task execution load to minimize the overall service time for delay-sensitive applications. Prior to task offloading, continuous device, network, and task profiling are performed, and for each task assigned, an edge with maximum utility is derived using a weighted utility maximization technique, and a system reward assignment for task connectivity or sensitivity is performed. A scheduler is in charge of task assignment, whereas an executor is responsible for task offloading on edge devices. Experimental comparisons of the proposed approach with conventional offloading methods indicate better performance in terms of optimizing resource utilization and minimizing task latency.}, keywords = {computing, multi-robot, networking}, pubstate = {published}, tppubtype = {conference} } To circumvent persistent connectivity to the cloud infrastructure, the current emphasis on computing at network edge devices in the multi-robot domain is a promising enabler for delay-sensitive jobs, yet its adoption is rife with challenges. This paper proposes a novel utility-aware dynamic task offloading strategy based on a multi-edge-robot system that takes into account computation, communication, and task execution load to minimize the overall service time for delay-sensitive applications. Prior to task offloading, continuous device, network, and task profiling are performed, and for each task assigned, an edge with maximum utility is derived using a weighted utility maximization technique, and a system reward assignment for task connectivity or sensitivity is performed. A scheduler is in charge of task assignment, whereas an executor is responsible for task offloading on edge devices. Experimental comparisons of the proposed approach with conventional offloading methods indicate better performance in terms of optimizing resource utilization and minimizing task latency. |
 | SEAL: Simultaneous Exploration and Localization in Multi-Robot Systems Conference 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2023), IEEE 2023. Abstract | Links | BibTeX | Tags: cooperation, localization, mapping, multi-robot @conference{Latif2023b, title = {SEAL: Simultaneous Exploration and Localization in Multi-Robot Systems}, author = {Ehsan Latif and Ramviyas Parasuraman}, url = {https://ieeexplore.ieee.org/document/10342157}, doi = {10.1109/IROS55552.2023.10342157}, year = {2023}, date = {2023-10-05}, booktitle = {2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2023)}, organization = {IEEE}, abstract = {The availability of accurate localization is critical for multi-robot exploration strategies; noisy or inconsistent localization causes failure in meeting exploration objectives. We aim to achieve high localization accuracy with contemporary exploration map belief and vice versa without needing global localization information. This paper proposes a novel simultaneous exploration and localization (SEAL) approach, which uses Gaussian Processes (GP)-based information fusion for maximum exploration while performing communication graph optimization for relative localization. Both these cross-dependent objectives were integrated through the Rao-Blackwellization technique. Distributed linearized convex hull optimization is used to select the next-best unexplored region for distributed exploration. SEAL outperformed cutting-edge methods on exploration and localization performance in extensive ROS-Gazebo simulations, illustrating the practicality of the approach in real-world applications.}, keywords = {cooperation, localization, mapping, multi-robot}, pubstate = {published}, tppubtype = {conference} } The availability of accurate localization is critical for multi-robot exploration strategies; noisy or inconsistent localization causes failure in meeting exploration objectives. We aim to achieve high localization accuracy with contemporary exploration map belief and vice versa without needing global localization information. This paper proposes a novel simultaneous exploration and localization (SEAL) approach, which uses Gaussian Processes (GP)-based information fusion for maximum exploration while performing communication graph optimization for relative localization. Both these cross-dependent objectives were integrated through the Rao-Blackwellization technique. Distributed linearized convex hull optimization is used to select the next-best unexplored region for distributed exploration. SEAL outperformed cutting-edge methods on exploration and localization performance in extensive ROS-Gazebo simulations, illustrating the practicality of the approach in real-world applications. |
 | Systems Design Concepts mimicking Bio-inspired Self-assembly Conference 9th International Conference on Research Into Design (ICoRD), Springer, 2023. Abstract | Links | BibTeX | Tags: behavior-trees, design, multiagent-systems @conference{Venkata2023, title = {Systems Design Concepts mimicking Bio-inspired Self-assembly}, author = {Sanjay Sarma Oruganti Venkata and Cameron Ardoin and Israr M. Ibrahim and Ramviyas Parasuraman and Ramana M Pidaparti}, url = {https://link.springer.com/chapter/10.1007/978-981-99-0428-0_31}, doi = {10.1007/978-981-99-0428-0_31}, year = {2023}, date = {2023-07-25}, booktitle = {9th International Conference on Research Into Design (ICoRD)}, publisher = {Springer}, abstract = {Design of complex self-assembly systems requires intelligent solutions that can be manufactured effectively and efficiently. Self-organization is the spontaneous formation of organized structures that can dynamically reconfigure with changing environments. These processes are primarily observed in chemical and biological processes that resemble large-scale ecosystems and in environments as small as biological cells. Inspired by these natural processes, there is also a growing research interest in developing 4D Design and printing technologies in which 3D structures reconfigure with changing stimuli. The 4D design process requires appropriate design, computational and simulation tools aimed at building structures at larger scales that can augment the current engineering design and manufacturing processes. This study presents a new multi-agent framework with two new paradigms called agents-as-blocks and free-agent. We present further details on these new strategies in the form of preliminary case studies applied to simulating micro-environments of microtubules’ self-organization process and through a vibration simulation platform. Our simulation results closely follow the real formation patterns in the microtubules process and show some interesting self-organizing and self-assembling patterns that change with varying geometries, rules, and stimuli in a vibration-platform environment.}, keywords = {behavior-trees, design, multiagent-systems}, pubstate = {published}, tppubtype = {conference} } Design of complex self-assembly systems requires intelligent solutions that can be manufactured effectively and efficiently. Self-organization is the spontaneous formation of organized structures that can dynamically reconfigure with changing environments. These processes are primarily observed in chemical and biological processes that resemble large-scale ecosystems and in environments as small as biological cells. Inspired by these natural processes, there is also a growing research interest in developing 4D Design and printing technologies in which 3D structures reconfigure with changing stimuli. The 4D design process requires appropriate design, computational and simulation tools aimed at building structures at larger scales that can augment the current engineering design and manufacturing processes. This study presents a new multi-agent framework with two new paradigms called agents-as-blocks and free-agent. We present further details on these new strategies in the form of preliminary case studies applied to simulating micro-environments of microtubules’ self-organization process and through a vibration simulation platform. Our simulation results closely follow the real formation patterns in the microtubules process and show some interesting self-organizing and self-assembling patterns that change with varying geometries, rules, and stimuli in a vibration-platform environment. |
 | Communication-Efficient Reinforcement Learning in Swarm Robotic Networks for Maze Exploration Conference IEEE INFOCOM 2023 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), 2023. Abstract | Links | BibTeX | Tags: cooperation, mapping, multi-robot, multi-robot systems, networking @conference{Latif2023, title = {Communication-Efficient Reinforcement Learning in Swarm Robotic Networks for Maze Exploration}, author = {Ehsan Latif and WenZhan Song and Ramviyas Parasuraman}, doi = {10.1109/INFOCOMWKSHPS57453.2023.10226167}, year = {2023}, date = {2023-05-01}, booktitle = {IEEE INFOCOM 2023 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)}, abstract = {Smooth coordination within a swarm robotic system is essential for the effective execution of collective robot missions. Having efficient communication is key to the successful coordination of swarm robots. This paper proposes a new communication-efficient decentralized cooperative reinforcement learning algorithm for coordinating swarm robots. It is made efficient by hierarchically building on the use of local information exchanges. We consider a case study application of maze solving through cooperation among a group of robots, where the time and costs are minimized while avoiding inter-robot collisions and path overlaps during exploration. With a solid theoretical basis, we extensively analyze the algorithm with realistic CORE network simulations and evaluate it against state-of-the-art solutions in terms of maze coverage percentage and efficiency under communication-degraded environments. The results demonstrate significantly higher coverage accuracy and efficiency while reducing costs and overlaps even in high packet loss and low communication range scenarios.}, keywords = {cooperation, mapping, multi-robot, multi-robot systems, networking}, pubstate = {published}, tppubtype = {conference} } Smooth coordination within a swarm robotic system is essential for the effective execution of collective robot missions. Having efficient communication is key to the successful coordination of swarm robots. This paper proposes a new communication-efficient decentralized cooperative reinforcement learning algorithm for coordinating swarm robots. It is made efficient by hierarchically building on the use of local information exchanges. We consider a case study application of maze solving through cooperation among a group of robots, where the time and costs are minimized while avoiding inter-robot collisions and path overlaps during exploration. With a solid theoretical basis, we extensively analyze the algorithm with realistic CORE network simulations and evaluate it against state-of-the-art solutions in terms of maze coverage percentage and efficiency under communication-degraded environments. The results demonstrate significantly higher coverage accuracy and efficiency while reducing costs and overlaps even in high packet loss and low communication range scenarios. |
 | The 38th ACM/SIGAPP Symposium On Applied Computing, IRMAS 2023, (Oral Presentation. Acceptance Rate: <25%). Abstract | Links | BibTeX | Tags: cooperation, multi-robot-systems, multiagent-systems, planning @conference{Yang2023, title = {A hierarchical game-theoretic decision-making for cooperative multiagent systems under the presence of adversarial agents}, author = {Qin Yang and Ramviyas Parasuraman}, url = {https://acmsac-irmas2023.isr.uc.pt/index.php/track-program}, year = {2023}, date = {2023-03-31}, booktitle = {The 38th ACM/SIGAPP Symposium On Applied Computing}, series = {IRMAS}, abstract = {Underlying relationships among Multi-Agent Systems (MAS) in hazardous scenarios can be represented as Game-theoretic models. This paper proposes a new hierarchical network-based model called Game-theoretic Utility Tree (GUT), which decomposes high-level strategies into executable low-level actions for cooperative MAS decisions. It combines with a new payoff measure based on agent needs for real-time strategy games. We present an Explore game domain, where we measure the performance of MAS achieving tasks from the perspective of balancing the success probability and system costs. We evaluate the GUT approach against state-of-the-art methods that greedily rely on the rewards of the composite actions. Conclusive results on extensive numerical simulations indicate that GUT can organize more complex relationships among MAS cooperation, helping the group achieve challenging tasks with lower costs and higher winning rates. Furthermore, we demonstrated the applicability of the GUT using the simulator-hardware testbed - Robotarium. The performances verified the effectiveness of the GUT in the real robot application and validated that the GUT could effectively organize MAS cooperation strategies, helping the group with fewer advantages achieve higher performance.}, note = {Oral Presentation. Acceptance Rate: <25%}, keywords = {cooperation, multi-robot-systems, multiagent-systems, planning}, pubstate = {published}, tppubtype = {conference} } Underlying relationships among Multi-Agent Systems (MAS) in hazardous scenarios can be represented as Game-theoretic models. This paper proposes a new hierarchical network-based model called Game-theoretic Utility Tree (GUT), which decomposes high-level strategies into executable low-level actions for cooperative MAS decisions. It combines with a new payoff measure based on agent needs for real-time strategy games. We present an Explore game domain, where we measure the performance of MAS achieving tasks from the perspective of balancing the success probability and system costs. We evaluate the GUT approach against state-of-the-art methods that greedily rely on the rewards of the composite actions. Conclusive results on extensive numerical simulations indicate that GUT can organize more complex relationships among MAS cooperation, helping the group achieve challenging tasks with lower costs and higher winning rates. Furthermore, we demonstrated the applicability of the GUT using the simulator-hardware testbed - Robotarium. The performances verified the effectiveness of the GUT in the real robot application and validated that the GUT could effectively organize MAS cooperation strategies, helping the group with fewer advantages achieve higher performance. |
 | Mobile Robot Control and Autonomy Through Collaborative Twin Conference 2023 IEEE PerCom - International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events, 2023. Abstract | Links | BibTeX | Tags: autonomy, cooperation, networking @conference{Tahir2023, title = {Mobile Robot Control and Autonomy Through Collaborative Twin}, author = {Nazish Tahir and Ramviyas Parasuraman}, doi = { 10.1109/PerComWorkshops56833.2023.10150325}, year = {2023}, date = {2023-03-17}, booktitle = {2023 IEEE PerCom - International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events}, abstract = {When a mobile robot lacks high onboard computing or networking capabilities, it can rely on remote computing architecture for its control and autonomy. In this paper, we introduce a novel collaborative twin strategy for control and autonomy on resource-constrained robots. The practical implementation of such a strategy entails a mobile robot system divided into a cyber (simulated) and physical (real) space separated over a communication channel where the physical robot resides on the site of operation guided by a simulated autonomous agent from a remote location maintained over a network. Building on top of the digital twin concept, our collaboration twin is capable of autonomous navigation through an advanced SLAM-based path planning algorithm, while the physical robot is capable of tracking the Simulated twin's velocity and communicating feedback generated through interaction with its environment. We proposed a prioritized path planning application to the test in a collaborative teleoperation system of a physical robot guided by Simulation Twin's autonomous navigation. We examine the performance of a physical robot led by autonomous navigation from the Collaborative Twin and assisted by a predicted force received from the physical robot. The experimental findings indicate the practicality of the proposed simulation-physical twinning approach and provide computational and network performance improvements compared to typical remote computing and digital twin approaches.}, keywords = {autonomy, cooperation, networking}, pubstate = {published}, tppubtype = {conference} } When a mobile robot lacks high onboard computing or networking capabilities, it can rely on remote computing architecture for its control and autonomy. In this paper, we introduce a novel collaborative twin strategy for control and autonomy on resource-constrained robots. The practical implementation of such a strategy entails a mobile robot system divided into a cyber (simulated) and physical (real) space separated over a communication channel where the physical robot resides on the site of operation guided by a simulated autonomous agent from a remote location maintained over a network. Building on top of the digital twin concept, our collaboration twin is capable of autonomous navigation through an advanced SLAM-based path planning algorithm, while the physical robot is capable of tracking the Simulated twin's velocity and communicating feedback generated through interaction with its environment. We proposed a prioritized path planning application to the test in a collaborative teleoperation system of a physical robot guided by Simulation Twin's autonomous navigation. We examine the performance of a physical robot led by autonomous navigation from the Collaborative Twin and assisted by a predicted force received from the physical robot. The experimental findings indicate the practicality of the proposed simulation-physical twinning approach and provide computational and network performance improvements compared to typical remote computing and digital twin approaches. |
 | A Strategy-Oriented Bayesian Soft Actor-Critic Model Conference Procedia Computer Science, 220 , ANT 2023 Elsevier, 2023. Abstract | Links | BibTeX | Tags: autonomy, learning @conference{Yang2023b, title = {A Strategy-Oriented Bayesian Soft Actor-Critic Model}, author = {Qin Yang and Ramviyas Parasuraman}, url = {https://www.sciencedirect.com/science/article/pii/S1877050923006063}, doi = {10.1016/j.procs.2023.03.071}, year = {2023}, date = {2023-03-17}, booktitle = {Procedia Computer Science}, journal = {Procedia Computer Science}, volume = {220}, pages = {561-566}, publisher = {Elsevier}, series = {ANT 2023}, abstract = {Adopting reasonable strategies is challenging but crucial for an intelligent agent with limited resources working in hazardous, unstructured, and dynamic environments to improve the system's utility, decrease the overall cost, and increase mission success probability. This paper proposes a novel hierarchical strategy decomposition approach based on the Bayesian chain rule to separate an intricate policy into several simple sub-policies and organize their relationships as Bayesian strategy networks (BSN). We integrate this approach into the state-of-the-art DRL method – soft actor-critic (SAC) and build the corresponding Bayesian soft actor-critic (BSAC) model by organizing several sub-policies as a joint policy. We compare the proposed BSAC method with the SAC and other state-of-the-art approaches such as TD3, DDPG, and PPO on the standard continuous control benchmarks – Hopper-v2, Walker2d-v2, and Humanoid-v2 – in MuJoCo with the OpenAI Gym environment. The results demonstrate that the promising potential of the BSAC method significantly improves training efficiency.}, keywords = {autonomy, learning}, pubstate = {published}, tppubtype = {conference} } Adopting reasonable strategies is challenging but crucial for an intelligent agent with limited resources working in hazardous, unstructured, and dynamic environments to improve the system's utility, decrease the overall cost, and increase mission success probability. This paper proposes a novel hierarchical strategy decomposition approach based on the Bayesian chain rule to separate an intricate policy into several simple sub-policies and organize their relationships as Bayesian strategy networks (BSN). We integrate this approach into the state-of-the-art DRL method – soft actor-critic (SAC) and build the corresponding Bayesian soft actor-critic (BSAC) model by organizing several sub-policies as a joint policy. We compare the proposed BSAC method with the SAC and other state-of-the-art approaches such as TD3, DDPG, and PPO on the standard continuous control benchmarks – Hopper-v2, Walker2d-v2, and Humanoid-v2 – in MuJoCo with the OpenAI Gym environment. The results demonstrate that the promising potential of the BSAC method significantly improves training efficiency. |
 | HeRoSwarm: Fully-Capable Miniature Swarm Robot Hardware Design With Open-Source ROS Support Conference 2023 IEEE/SICE International Symposium on System Integrations (SII 2023) , IEEE, 2023. Abstract | Links | BibTeX | Tags: control, multi-robot, swarm-robotics @conference{Starks2023, title = {HeRoSwarm: Fully-Capable Miniature Swarm Robot Hardware Design With Open-Source ROS Support}, author = {Michael Starks and Aryan Gupta and Sanjay Sarma O V and Ramviyas Parasuraman}, url = {https://ieeexplore.ieee.org/document/10039174}, doi = {10.1109/SII55687.2023.10039174}, year = {2023}, date = {2023-01-23}, booktitle = {2023 IEEE/SICE International Symposium on System Integrations (SII 2023) }, publisher = {IEEE}, abstract = {Experiments using large numbers of miniature swarm robots are desirable to teach, study, and test multi-robot and swarm intelligence algorithms and their applications. To realize the full potential of a swarm robot, it should be capable of not only motion but also sensing, computing, communication, and power management modules with multiple options. Current swarm robot platforms developed for commercial and academic research purposes lack several of these important attributes by focusing on one of these aspects. Therefore, in this paper, we propose the HeRoSwarm, a fully-capable swarm robot platform, with open-source hardware and software support. The proposed robot hardware is a low-cost design with commercial off-the-shelf components that uniquely integrates multiple sensing, communication, and computing modalities with various power management capabilities into a tiny footprint. Moreover, our swarm robot with odometry capability with Robot Operating Systems (ROS) support is unique in its kind. This simple, yet powerful swarm robot design has been extensively verified with different prototyping variants and multi-robot experimental demonstrations.}, keywords = {control, multi-robot, swarm-robotics}, pubstate = {published}, tppubtype = {conference} } Experiments using large numbers of miniature swarm robots are desirable to teach, study, and test multi-robot and swarm intelligence algorithms and their applications. To realize the full potential of a swarm robot, it should be capable of not only motion but also sensing, computing, communication, and power management modules with multiple options. Current swarm robot platforms developed for commercial and academic research purposes lack several of these important attributes by focusing on one of these aspects. Therefore, in this paper, we propose the HeRoSwarm, a fully-capable swarm robot platform, with open-source hardware and software support. The proposed robot hardware is a low-cost design with commercial off-the-shelf components that uniquely integrates multiple sensing, communication, and computing modalities with various power management capabilities into a tiny footprint. Moreover, our swarm robot with odometry capability with Robot Operating Systems (ROS) support is unique in its kind. This simple, yet powerful swarm robot design has been extensively verified with different prototyping variants and multi-robot experimental demonstrations. |
 | Energy-Aware Coverage Planning for Heterogeneous Multi-Robot System Workshop IEEE International Symposium on Multi-Robot & Multi-Agent Systems (MRS) 2023, 2023, (Presented as Poster Paper). Abstract | Links | BibTeX | Tags: control, multi-robot, planning @workshop{Munir2023, title = {Energy-Aware Coverage Planning for Heterogeneous Multi-Robot System}, author = {Aiman Munir, Ayan Dutta, and Ramviyas Parasuraman}, url = {https://sites.bu.edu/mrs2023/program/list-of-accepted-papers-and-presentations/}, year = {2023}, date = {2023-12-06}, booktitle = {IEEE International Symposium on Multi-Robot & Multi-Agent Systems (MRS) 2023}, abstract = {We propose a distributed control law for a heterogeneous multi-robot coverage problem, where the robots could have different energy depletion rates due to their varying sizes, speeds, capabilities, and payloads. Existing energy-aware coverage control laws assume the battery depletion rate to be the same for all robots. In realistic scenarios, however, some robots can consume energy much faster than other robots, for instance, UAVs hovering at different altitudes. Robots' energy capacities and depletion rates need to be considered to maximize the performance of a multi-robot system. To this end, we propose a new energy-aware controller based on Lloyd's algorithm to adapt the weights of the robots based on their energy needs and divide the area of interest among the robots accordingly. The controller is theoretically analyzed and extensively evaluated through simulations in multiple realistic scenarios and compared with three baseline control laws to validate its performance and efficacy.}, note = {Presented as Poster Paper}, keywords = {control, multi-robot, planning}, pubstate = {published}, tppubtype = {workshop} } We propose a distributed control law for a heterogeneous multi-robot coverage problem, where the robots could have different energy depletion rates due to their varying sizes, speeds, capabilities, and payloads. Existing energy-aware coverage control laws assume the battery depletion rate to be the same for all robots. In realistic scenarios, however, some robots can consume energy much faster than other robots, for instance, UAVs hovering at different altitudes. Robots' energy capacities and depletion rates need to be considered to maximize the performance of a multi-robot system. To this end, we propose a new energy-aware controller based on Lloyd's algorithm to adapt the weights of the robots based on their energy needs and divide the area of interest among the robots accordingly. The controller is theoretically analyzed and extensively evaluated through simulations in multiple realistic scenarios and compared with three baseline control laws to validate its performance and efficacy. |
Publications
2026 |
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| FRESHR-GSI: A Generalized Safety Model and Evaluation Framework for Mobile Robots in Multi-Human Environments Conference Forthcoming 2026 IEEE International Conference on Robotics & Automation (ICRA), Forthcoming. |
| DCL-Sparse: Distributed Relative Localization in Sparse Graphs Conference Forthcoming 2026 IEEE International Conference on Robotics & Automation (ICRA), Forthcoming. |
| Multi-Robot Informative Sampling and Coverage in GPS-Denied Environments Conference Forthcoming 2026 IEEE International Conference on Robotics & Automation (ICRA), Forthcoming. |
| Imitation-BT: Automating Behavior Tree Generation by Echoing Reinforcement Learning Agents Conference Forthcoming 2026 IEEE International Conference on Robotics & Automation (ICRA), Forthcoming. |
| Energy-Aware Informative Path Planning for Heterogeneous Multi-Robot Systems Conference Forthcoming 2026 IEEE International Conference on Robotics & Automation (ICRA), Forthcoming. |
2025 |
|
| SPACE: 3D Spatial Co-operation and Exploration Framework for Robust Mapping and Coverage with Multi-Robot Systems Journal Article IEEE Robotics and Automation Letters, 10 (12), pp. 13074–13081, 2025. |
| Real-World Cyber Security Demonstration for Networked Electric Drives Journal Article IEEE Journal of Emerging and Selected Topics in Power Electronics, 13 (4), 2025. |
| Edge Computing and its Application in Robotics: A Survey Journal Article Journal of Sensor and Actuator Networks, 14 (4), 2025. |
| IKT-BT: Indirect Knowledge Transfer Behavior Tree Framework for Multirobot Systems Through Communication Eavesdropping Journal Article IEEE Transactions on Cybernetics, 2025. |
| Online Adaptive Anomaly Detection in Networked Electrical Machines by Adaptive Enveloped Singular Spectrum Transformation Journal Article IEEE Internet of Things Journal, 12 (6), pp. 6457-646, 2025. |
| Autonomous Navigation of a Quadruped Robot to Approach Floor Eggs and Path Optimization Analysis for Commercial Feasibility Journal Article American Society of Agricultural and Biological Engineers, 41 (6), pp. 733-747, 2025. |
| 2025 IEEE International Symposium on Multi-Robot and Multi-Agent Systems (MRS), 2025. |
| Anonymous Distributed Localisation via Spatial Population Protocols Conference International Symposium on Algorithms and Computation (ISAAC 2025)., 2025. |
| Analyzing Human Perceptions of a MEDEVAC Robot in a Simulated Evacuation Scenario Conference 2025 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2025. |
| Distributed Fault-Tolerant Multi-Robot Cooperative Localization in Adversarial Environments Conference 2025 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2025. |
| 2025 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2025. |
| Integrating Perceptions: A Human-Centered Physical Safety Model for Human-Robot Interaction Conference 2025 34th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), 2025. |
| Brief Announcement: Anonymous Distributed Localisation via Spatial Population Protocols Conference 4th Symposium on Algorithmic Foundations of Dynamic Networks (SAND 2025), 2025. |
| 2025 IEEE International Conference on Robotics and Automation (ICRA), 2025. |
| GSI- A Proxemics-Guided Generalized Safety Metric For Evaluating Safety in Social Navigation Context Workshop IEEE ICRA 2025 Workshop on Advances in Social Navigation: Planning, HRI and Beyond, 2025, (Received Best Poster Award.). |
| H-Cov: Multi-UAV Sensor Coverage with Altitude Optimization for Target Tracking Workshop IEEE ICRA 2025 Workshop on 25 YEARS OF AERIAL ROBOTICS: CHALLENGES AND OPPORTUNITIES, 2025. |
| IEEE ICRA 2025 Workshop on Block by Block Collaborative Strategies for Multi-agent Robotic Construction, 2025. |
2024 |
|
| Bayesian Strategy Networks Based Soft Actor-Critic Learning Journal Article ACM Transactions on Intelligent Systems and Technology, 15 (3), pp. 1–24, 2024. |
| Communication-Efficient Multi-Robot Exploration Using Coverage-biased Distributed Q-Learning Journal Article IEEE Robotics and Automation Letters, 9 (3), pp. 2622 - 2629, 2024. |
| Instantaneous Wireless Robotic Node Localization Using Collaborative Direction of Arrival Journal Article IEEE Internet of Things Journal, 11 (2), pp. 2783 - 2795, 2024. |
| Energy-Aware Coverage Planning for Heterogeneous Multi-Robot System Conference The 17th International Symposium on Distributed Autonomous Robotic Systems (DARS) 2024, 2024, (In Press). |
| 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2024), 2024. |
| 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2024), 2024. |
| Anchor-Oriented Localized Voronoi Partitioning for GPS-denied Multi-Robot Coverage Conference 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2024), 2024. |
| Route Planning for Electric Vehicles with Charging Constraints Conference 2024 IEEE 100th Vehicular Technology Conference (VTC2024-Fall), 2024. |
| Communication-Aware Consistent Edge Selection for Mobile Users and Autonomous Vehicles Conference 2024 IEEE 100th Vehicular Technology Conference (VTC2024-Fall), 2024. |
| PhysicsAssistant: An LLM-Powered Interactive Learning Robot for Physics Lab Investigations Conference The 33rd IEEE International Conference on Robot and Human Interactive Communication, IEEE RO-MAN 2024, 2024. |
| Map2Schedule: An End-to-End Link Scheduling Method for Urban V2V Communications Conference 2024 IEEE International Conference on Communications (ICC), 2024, (Accepted for Presentation at ICC 2024). |
| Bayesian Soft Actor-Critic: A Directed Acyclic Strategy Graph Based Deep Reinforcement Learning Conference 2024 ACM/SIGAPP Symposium on Applied Computing (SAC) , IRMAS Track 2024. |
| Anchor-oriented Multi-Robot Coverage without Global Localization Workshop IEEE ICRA 2024 Workshop on Sensing and Perception in Extreme Environments (HERMES), 2024, (Spotlight Presentation). |
| PhysicsAssistant: An LLM-Powered Interactive Learning Robot for Physics Lab Investigations Workshop IEEE ICRA 2024 Workshop on Accelerating Discovery in Natural Science Laboratories with AI and Robotics, 2024, (Selected for the Pioneer Award). |
2023 |
|
| Exploration–Exploitation Tradeoff in the Adaptive Information Sampling of Unknown Spatial Fields with Mobile Robots Journal Article Sensors, 23 (23), 2023. |
| On the Intersection of Computational Geometry Algorithms with Mobile Robot Path Planning Journal Article Algorithms, 16 (11), pp. 498, 2023. |
| KT-BT: A Framework for Knowledge Transfer Through Behavior Trees in Multi-Robot Systems Journal Article IEEE Transactions on Robotics, 30 (5), pp. 4114 - 4130, 2023. |
| Rapid prediction of network quality in mobile robots Journal Article Ad Hoc Networks, 138 , 2023, ISSN: 1570-8705. |
| Consensus-based Resource Scheduling for Collaborative Multi-Robot Tasks Conference 2023 Sixth IEEE International Conference on Robotic Computing (IRC), 2023. |
| Utility AI for Dynamic Task Offloading in the Multi-Edge Infrastructure Conference 2023 Sixth IEEE International Conference on Robotic Computing (IRC), 2023. |
| SEAL: Simultaneous Exploration and Localization in Multi-Robot Systems Conference 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2023), IEEE 2023. |
| Systems Design Concepts mimicking Bio-inspired Self-assembly Conference 9th International Conference on Research Into Design (ICoRD), Springer, 2023. |
| Communication-Efficient Reinforcement Learning in Swarm Robotic Networks for Maze Exploration Conference IEEE INFOCOM 2023 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), 2023. |
| The 38th ACM/SIGAPP Symposium On Applied Computing, IRMAS 2023, (Oral Presentation. Acceptance Rate: <25%). |
| Mobile Robot Control and Autonomy Through Collaborative Twin Conference 2023 IEEE PerCom - International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events, 2023. |
| A Strategy-Oriented Bayesian Soft Actor-Critic Model Conference Procedia Computer Science, 220 , ANT 2023 Elsevier, 2023. |
| HeRoSwarm: Fully-Capable Miniature Swarm Robot Hardware Design With Open-Source ROS Support Conference 2023 IEEE/SICE International Symposium on System Integrations (SII 2023) , IEEE, 2023. |
| Energy-Aware Coverage Planning for Heterogeneous Multi-Robot System Workshop IEEE International Symposium on Multi-Robot & Multi-Agent Systems (MRS) 2023, 2023, (Presented as Poster Paper). |