2026 |
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 | SK: Semantic Kernel for Robotic Information Gathering Conference Forthcoming 2026 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Forthcoming. Abstract | Links | BibTeX | Tags: autonomy, cooperation, mapping, multi-robot systems @conference{Ghanta2026, title = {SK: Semantic Kernel for Robotic Information Gathering}, author = {Sai Krishna Ghanta and Ramviyas Parasuraman}, url = {https://herolab.org/ghanta_iros_2026_semantic_kernel_for_robotic_information_sampling_preprint/}, year = {2026}, date = {2026-09-27}, booktitle = {2026 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}, abstract = {Environmental spatial phenomena exhibit rich semantic and structural relationships, yet most robotic information gathering methods assume stationarity and ignore scene context. Meanwhile, Large Language Models (LLMs) demonstrate strong spatial reasoning capabilities, yet their potential for modeling spatial information remains largely unexplored. We propose a Semantic Kernel (SK) for Gaussian Processes (GPs) that integrates scene semantics into environmental field estimation. Specifically, we leverage an LLM to interpret scene graphs and extract structural relationships, which are then used to construct spatially varying priors, yielding a non-stationary GP that adapts to scene context. To efficiently adapt kernel parameterization from semantics with minimal supervision, we develop a hybrid approach that combines few-shot learning with parameter-efficient fine-tuning, enabling the LLM to learn heterogeneous semantic priors across diverse environments. We evaluate the proposed approach extensively in both simulation and real-world deployments against state-of-the-art baselines. These findings highlight the potential of combining semantic scene reasoning with probabilistic modeling to enable more efficient and reliable robotic information gathering.}, keywords = {autonomy, cooperation, mapping, multi-robot systems}, pubstate = {forthcoming}, tppubtype = {conference} } Environmental spatial phenomena exhibit rich semantic and structural relationships, yet most robotic information gathering methods assume stationarity and ignore scene context. Meanwhile, Large Language Models (LLMs) demonstrate strong spatial reasoning capabilities, yet their potential for modeling spatial information remains largely unexplored. We propose a Semantic Kernel (SK) for Gaussian Processes (GPs) that integrates scene semantics into environmental field estimation. Specifically, we leverage an LLM to interpret scene graphs and extract structural relationships, which are then used to construct spatially varying priors, yielding a non-stationary GP that adapts to scene context. To efficiently adapt kernel parameterization from semantics with minimal supervision, we develop a hybrid approach that combines few-shot learning with parameter-efficient fine-tuning, enabling the LLM to learn heterogeneous semantic priors across diverse environments. We evaluate the proposed approach extensively in both simulation and real-world deployments against state-of-the-art baselines. These findings highlight the potential of combining semantic scene reasoning with probabilistic modeling to enable more efficient and reliable robotic information gathering. |
 | Distributed Connectivity Restoration and Improvement in Networked Robots Conference Forthcoming 2026 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Forthcoming. Abstract | Links | BibTeX | Tags: control, cooperation, multi-robot systems, networking @conference{Sagale2026b, title = {Distributed Connectivity Restoration and Improvement in Networked Robots}, author = {Atharva Sagale and Ramviyas Parasuraman}, url = {https://herolab.org/iros26_sagale_dfcr/}, year = {2026}, date = {2026-09-27}, booktitle = {2026 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}, abstract = {Multi-robot coordination requires maintaining network connectivity, especially in critical operations such as search and rescue, where network robustness is paramount. In this paper, we study the Fast k-connectivity Restoration problem (FCR), which aims to minimize the maximum distance required to restore connectivity. Recent works have proposed scalable solutions, but they rely on a centralized architecture or a learning-based solution to transfer the observable policies from centralized to distributed variants. However, computing node connectivity in a distributed setting is challenging, and performing such operations in a deterministic, algorithmic manner is critical for generalizability and persistent deployment in diverse, unknown environments. We propose a distributed control approach for improving and restoring connectivity. Using local neighborhood interactions and information, our algorithm improves the degrees of individual robots by augmenting 1-hop edges based on their distances, achieving the desired level of node connectivity and yielding comparable performance to achieve sparse connectivity improvements and up to 58% reduction in movements required to achieve dense connectivity over the state-of-the-art algorithms. }, keywords = {control, cooperation, multi-robot systems, networking}, pubstate = {forthcoming}, tppubtype = {conference} } Multi-robot coordination requires maintaining network connectivity, especially in critical operations such as search and rescue, where network robustness is paramount. In this paper, we study the Fast k-connectivity Restoration problem (FCR), which aims to minimize the maximum distance required to restore connectivity. Recent works have proposed scalable solutions, but they rely on a centralized architecture or a learning-based solution to transfer the observable policies from centralized to distributed variants. However, computing node connectivity in a distributed setting is challenging, and performing such operations in a deterministic, algorithmic manner is critical for generalizability and persistent deployment in diverse, unknown environments. We propose a distributed control approach for improving and restoring connectivity. Using local neighborhood interactions and information, our algorithm improves the degrees of individual robots by augmenting 1-hop edges based on their distances, achieving the desired level of node connectivity and yielding comparable performance to achieve sparse connectivity improvements and up to 58% reduction in movements required to achieve dense connectivity over the state-of-the-art algorithms. |
 | H-IPP: Altitude-Adaptive Informative Path Planning with Multiple UAVs Conference Forthcoming 2026 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Forthcoming. Abstract | Links | BibTeX | Tags: control, mapping, multi-robot systems @conference{Nistane2026, title = {H-IPP: Altitude-Adaptive Informative Path Planning with Multiple UAVs}, author = {Swaraj Nistane and Ramviyas Parasuraman}, url = {https://herolab.org/nistane_iros_2026_hipp/}, year = {2026}, date = {2026-09-27}, booktitle = {2026 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}, abstract = {We present an altitude-adaptive information-gathering framework for multiple unmanned aerial vehicles (UAVs) tasked with mapping an unknown, non-stationary spatial field subject to sensing uncertainties and field-of-view constraints. A critical challenge in such missions is managing the trade-off between sensor coverage (field of view) and measurement fidelity, as higher altitudes expand the spatial footprint at the cost of increased noise and reduced resolution. To address this, we model the environment using a heteroscedastic Gaussian Process (GP) equipped with an Attentive Kernel, which dynamically learns non-stationary spatial correlations and local lengthscales to capture complex environmental features. The proposed framework coordinates multi-UAV exploration through a weighted Voronoi partitioning strategy driven by learned kernel lengthscales, allocating sensing effort in proportion to spatial information density. Sensing actions are selected via a structured two-stage discrete maximization over spatial location and altitude, guided by a multiplicative utility function integrating coverage, expected information gain, and variability-driven exploitation. In addition to field reconstruction, the learned GP hyperparameters are used to extract a semantic variability map, enabling structure-aware region segmentation without supervised labels and supporting downstream heterogeneous robotic deployment. Experimental evaluation on high-resolution elevation-derived fields demonstrates improved reconstruction accuracy and reduced uncertainty compared to fixed-altitude and stationary-kernel baselines.}, keywords = {control, mapping, multi-robot systems}, pubstate = {forthcoming}, tppubtype = {conference} } We present an altitude-adaptive information-gathering framework for multiple unmanned aerial vehicles (UAVs) tasked with mapping an unknown, non-stationary spatial field subject to sensing uncertainties and field-of-view constraints. A critical challenge in such missions is managing the trade-off between sensor coverage (field of view) and measurement fidelity, as higher altitudes expand the spatial footprint at the cost of increased noise and reduced resolution. To address this, we model the environment using a heteroscedastic Gaussian Process (GP) equipped with an Attentive Kernel, which dynamically learns non-stationary spatial correlations and local lengthscales to capture complex environmental features. The proposed framework coordinates multi-UAV exploration through a weighted Voronoi partitioning strategy driven by learned kernel lengthscales, allocating sensing effort in proportion to spatial information density. Sensing actions are selected via a structured two-stage discrete maximization over spatial location and altitude, guided by a multiplicative utility function integrating coverage, expected information gain, and variability-driven exploitation. In addition to field reconstruction, the learned GP hyperparameters are used to extract a semantic variability map, enabling structure-aware region segmentation without supervised labels and supporting downstream heterogeneous robotic deployment. Experimental evaluation on high-resolution elevation-derived fields demonstrates improved reconstruction accuracy and reduced uncertainty compared to fixed-altitude and stationary-kernel baselines. |
 | Multi-Robot Consensus and Coordination with Localization Uncertainty Conference Forthcoming 2026 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Forthcoming. Abstract | Links | BibTeX | Tags: cooperation, localization, multi-robot systems @conference{Tasooji2026, title = {Multi-Robot Consensus and Coordination with Localization Uncertainty }, author = {Tohid Kargar Tasooji and Ramviyas Parasuraman}, url = {https://herolab.org/tasooji_iros_2026_wassersteincoordination/}, year = {2026}, date = {2026-09-27}, booktitle = {2026 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}, abstract = {Multi-robot coordination requires robots to share or estimate their relative positions using onboard sensors. However, the uncertainty in these position measurements is typically ignored in consensus-based protocols for rendezvous, formation control, and role assignment, which can degrade performance and safety in realistic environments. This paper presents a Wasserstein-based consensus and formation control framework that explicitly accounts for localization uncertainty by modeling each robot’s position as a Gaussian belief and using the Wasserstein distance in the coordination law. This uncertainty-aware design improves robustness to sensor noise and communication constraints and guarantees exponential convergence to a consensus/formation equilibrium, with a lower bound on the convergence rate expressed in terms of uncertainty bounds and network topology. To ensure safety under uncertain localization, we further integrate a distributed chance-constrained Control Barrier Functions (CBF)–Quadratic Program (QP) layer that uses belief covariances to enforce probabilistic collision avoidance and connectivity preservation while minimally modifying the nominal controller. Extensive simulations and hardware experiments demonstrate that, for both consensus and formation tasks, the proposed framework outperforms traditional Euclidean-distance-based controllers in high-uncertainty scenarios by achieving lower steady-state error while maintaining safe inter-robot distances.}, keywords = {cooperation, localization, multi-robot systems}, pubstate = {forthcoming}, tppubtype = {conference} } Multi-robot coordination requires robots to share or estimate their relative positions using onboard sensors. However, the uncertainty in these position measurements is typically ignored in consensus-based protocols for rendezvous, formation control, and role assignment, which can degrade performance and safety in realistic environments. This paper presents a Wasserstein-based consensus and formation control framework that explicitly accounts for localization uncertainty by modeling each robot’s position as a Gaussian belief and using the Wasserstein distance in the coordination law. This uncertainty-aware design improves robustness to sensor noise and communication constraints and guarantees exponential convergence to a consensus/formation equilibrium, with a lower bound on the convergence rate expressed in terms of uncertainty bounds and network topology. To ensure safety under uncertain localization, we further integrate a distributed chance-constrained Control Barrier Functions (CBF)–Quadratic Program (QP) layer that uses belief covariances to enforce probabilistic collision avoidance and connectivity preservation while minimally modifying the nominal controller. Extensive simulations and hardware experiments demonstrate that, for both consensus and formation tasks, the proposed framework outperforms traditional Euclidean-distance-based controllers in high-uncertainty scenarios by achieving lower steady-state error while maintaining safe inter-robot distances. |
 | Hybrid Framework for Multi-Robot Target Search in Unknown and Adversarial Environments Conference Forthcoming 2026 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Forthcoming. Abstract | Links | BibTeX | Tags: cooperation, learning, localization, multi-robot systems @conference{Tasooji2026c, title = {Hybrid Framework for Multi-Robot Target Search in Unknown and Adversarial Environments}, author = {Tohid Kargar Tasooji and Ramviyas Parasuraman}, url = {https://herolab.org/tasooji_iros_2026_hdmpc/}, year = {2026}, date = {2026-09-27}, booktitle = {2026 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}, abstract = {This paper presents a hybrid framework for multi-robot multi-target search in unknown, cluttered, and adversarial environments with limited sensing and intermittent communication. We employ a unified grid-based map encoding obstacles, coverage, target likelihood, and communication quality, together with a probabilistic adversarial model using layered Gaussian-like danger zones to capture the impact of varying adversary levels. On top of this representation, we develop an adaptive communication-aware, learning-based distributed MPC scheme that uses shared maps for cooperative trajectory planning under strong communication and learned inter-robot interaction models to predict neighbor states and maintain search efficiency when communication degrades, thereby reducing reliance on real-time data exchange. Extensive experiments on diverse maps with static and dynamic targets show that the proposed method reduces communication overhead and search time while improving prediction accuracy and overall resilience across different levels of adversaries. }, keywords = {cooperation, learning, localization, multi-robot systems}, pubstate = {forthcoming}, tppubtype = {conference} } This paper presents a hybrid framework for multi-robot multi-target search in unknown, cluttered, and adversarial environments with limited sensing and intermittent communication. We employ a unified grid-based map encoding obstacles, coverage, target likelihood, and communication quality, together with a probabilistic adversarial model using layered Gaussian-like danger zones to capture the impact of varying adversary levels. On top of this representation, we develop an adaptive communication-aware, learning-based distributed MPC scheme that uses shared maps for cooperative trajectory planning under strong communication and learned inter-robot interaction models to predict neighbor states and maintain search efficiency when communication degrades, thereby reducing reliance on real-time data exchange. Extensive experiments on diverse maps with static and dynamic targets show that the proposed method reduces communication overhead and search time while improving prediction accuracy and overall resilience across different levels of adversaries. |
 | Multi-Robot Informative Sampling and Coverage in GPS-Denied Environments Conference 2026 IEEE International Conference on Robotics & Automation (ICRA), 2026. Abstract | Links | 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}, url = {https://herolab.org/munir___icra_2026___multi_robot_informative_path_planning_without_global_localization/}, 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 = {published}, 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. |
 | DCL-Sparse: Distributed Relative Localization in Sparse Graphs Conference 2026 IEEE International Conference on Robotics & Automation (ICRA), 2026. 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 = {published}, 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. |
 | Energy-Aware Informative Path Planning for Heterogeneous Multi-Robot Systems Conference 2026 IEEE International Conference on Robotics & Automation (ICRA), 2026. Abstract | Links | 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}, url = {https://herolab.org/munir___icra_2026___energy_aware_ipp/}, 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 = {published}, 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. |
 | 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. |
 | 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} } |
 | 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. |
 | 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. |
 | 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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 | 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. |
 | 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. |
2023 |
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 | 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. |
2022 |
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 | DGORL: Distributed Graph Optimization based Relative Localization of Multi-Robot Systems Conference The 16th International Symposium on Distributed Autonomous Robotic Systems (DARS) 2022, 2022, (Nominated for the Best Student Paper Award). Abstract | BibTeX | Tags: localization, multi-robot systems @conference{Latif2022c, title = {DGORL: Distributed Graph Optimization based Relative Localization of Multi-Robot Systems}, author = {Ehsan Latif and Ramviyas Parasuraman}, year = {2022}, date = {2022-11-30}, booktitle = {The 16th International Symposium on Distributed Autonomous Robotic Systems (DARS) 2022}, abstract = {An optimization problem is at the heart of many robotics estimating, planning, and optimum control problems. Several attempts have been made at model-based multi-robot localization, and few have formulated the multi-robot collaborative localization problem as a factor graph problem to solve through graph optimization. Here, the optimization objective is to minimize the errors of estimating the relative location estimates in a distributed manner. Our novel graph-theoretic approach to solving this problem consists of three major components; (connectivity) graph formation, expansion through transition model, and optimization of relative poses. First, we estimate the relative pose-connectivity graph using the received signal strength between the connected robots, indicating relative ranges between them. Then, we apply a motion model to formulate graph expansion and optimize them using g$^2$o graph optimization as a distributed solver over dynamic networks. Finally, we theoretically analyze the algorithm and numerically validate its optimality and performance through extensive simulations. The results demonstrate the practicality of the proposed solution compared to a state-of-the-art algorithm for collaborative localization in multi-robot systems.}, note = {Nominated for the Best Student Paper Award}, keywords = {localization, multi-robot systems}, pubstate = {published}, tppubtype = {conference} } An optimization problem is at the heart of many robotics estimating, planning, and optimum control problems. Several attempts have been made at model-based multi-robot localization, and few have formulated the multi-robot collaborative localization problem as a factor graph problem to solve through graph optimization. Here, the optimization objective is to minimize the errors of estimating the relative location estimates in a distributed manner. Our novel graph-theoretic approach to solving this problem consists of three major components; (connectivity) graph formation, expansion through transition model, and optimization of relative poses. First, we estimate the relative pose-connectivity graph using the received signal strength between the connected robots, indicating relative ranges between them. Then, we apply a motion model to formulate graph expansion and optimize them using g$^2$o graph optimization as a distributed solver over dynamic networks. Finally, we theoretically analyze the algorithm and numerically validate its optimality and performance through extensive simulations. The results demonstrate the practicality of the proposed solution compared to a state-of-the-art algorithm for collaborative localization in multi-robot systems. |
 | Collaborative Control of Mobile Robots Using Analog Twin Framework Workshop ICRA 2022 Workshop on Intelligent Control Methods and Machine Learning Algorithms for Human-Robot Interaction and Assistive Robotics, 2022. Abstract | Links | BibTeX | Tags: control, multi-robot systems, networking @workshop{Tahir2022, title = {Collaborative Control of Mobile Robots Using Analog Twin Framework}, author = {Nazish Tahir and Ramviyas Parasuraman}, url = {https://sites.google.com/ualberta.ca/2022workshop-ai-for-hri-cr-ar}, year = {2022}, date = {2022-05-23}, booktitle = {ICRA 2022 Workshop on Intelligent Control Methods and Machine Learning Algorithms for Human-Robot Interaction and Assistive Robotics}, abstract = {Resource-constrained mobile robots that lack the capability to be completely autonomous can rely on a human or AI supervisor acting at a remote site (e.g., control station or cloud) for their control. Such a supervised autonomy or collaborative control of a robot poses high networking and computing capabilities requirements at both sites, which are not easy to achieve. This paper introduces and analyzes a new analog twin framework by synchronizing mobility between two mobile robots, where one robot acts as an analog twin to the other robot. We devise a novel collaborative priority-based bilateral teleoperation strategy for supervised goal navigation tasks to validate the proposed framework. The practical implementation of a supervised control strategy on this framework entails a mobile robot system divided into a master-client scheme over a communication channel where the Client robot resides on the site of operation guided by the Master robot through an agent (human or AI) from a remote location. The master robot controls the client robot with its autonomous navigation algorithm, which reacts to the predictive force received from the Client robot. We analyze the proposed strategy in terms of network performance (throughput), task performance (goal reach accuracy), task efficiency, and computing efficiency (CPU utilization). Real-world experiments demonstrate the method’s novelty and versatility in realizing more practical reactive and collaborative planning and control applications.}, keywords = {control, multi-robot systems, networking}, pubstate = {published}, tppubtype = {workshop} } Resource-constrained mobile robots that lack the capability to be completely autonomous can rely on a human or AI supervisor acting at a remote site (e.g., control station or cloud) for their control. Such a supervised autonomy or collaborative control of a robot poses high networking and computing capabilities requirements at both sites, which are not easy to achieve. This paper introduces and analyzes a new analog twin framework by synchronizing mobility between two mobile robots, where one robot acts as an analog twin to the other robot. We devise a novel collaborative priority-based bilateral teleoperation strategy for supervised goal navigation tasks to validate the proposed framework. The practical implementation of a supervised control strategy on this framework entails a mobile robot system divided into a master-client scheme over a communication channel where the Client robot resides on the site of operation guided by the Master robot through an agent (human or AI) from a remote location. The master robot controls the client robot with its autonomous navigation algorithm, which reacts to the predictive force received from the Client robot. We analyze the proposed strategy in terms of network performance (throughput), task performance (goal reach accuracy), task efficiency, and computing efficiency (CPU utilization). Real-world experiments demonstrate the method’s novelty and versatility in realizing more practical reactive and collaborative planning and control applications. |
2020 |
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 | Multi-Point Rendezvous in Multi-Robot Systems Journal Article IEEE Transactions on Cybernetics, 50 (1), pp. 310-323, 2020, ISBN: 2168-2275. Abstract | Links | BibTeX | Tags: control, herding, multi-robot systems, robotics @article{Parasuraman2018b, title = {Multi-Point Rendezvous in Multi-Robot Systems}, author = {Ramviyas Parasuraman and Jonghoek Kim and Shaocheng Luo and Byung-Cheol Min}, url = {https://ieeexplore.ieee.org/document/8472798}, doi = {10.1109/TCYB.2018.2868870}, isbn = {2168-2275}, year = {2020}, date = {2020-01-01}, journal = {IEEE Transactions on Cybernetics}, volume = {50}, number = {1}, pages = {310-323}, abstract = {Multi-robot rendezvous control and coordination strategies have garnered significant interest in recent years because of their potential applications in decentralized tasks. In this paper, we introduce a coordinate-free rendezvous control strategy to enable multiple robots to gather at different locations (dynamic leader robots) by tracking their hierarchy in a connected interaction graph. A key novelty in this strategy is the gathering of robots in different groups rather than at a single consensus point, motivated by autonomous multi-point recharging and flocking control problems. We show that the proposed rendezvous strategy guarantees convergence and maintains connectivity while accounting for practical considerations such as robots with limited speeds and an obstacle-rich environment. The algorithm is distributed and handles minor faults such as a broken immobile robot and a sudden link failure. In addition, we propose an approach that determines the locations of rendezvous points based on the connected interaction topology and indirectly optimizes the total energy consumption for rendezvous in all robots. Through extensive experiments with the Robotarium multi-robot testbed, we verified and demonstrated the effectiveness of our approach and its properties.}, keywords = {control, herding, multi-robot systems, robotics}, pubstate = {published}, tppubtype = {article} } Multi-robot rendezvous control and coordination strategies have garnered significant interest in recent years because of their potential applications in decentralized tasks. In this paper, we introduce a coordinate-free rendezvous control strategy to enable multiple robots to gather at different locations (dynamic leader robots) by tracking their hierarchy in a connected interaction graph. A key novelty in this strategy is the gathering of robots in different groups rather than at a single consensus point, motivated by autonomous multi-point recharging and flocking control problems. We show that the proposed rendezvous strategy guarantees convergence and maintains connectivity while accounting for practical considerations such as robots with limited speeds and an obstacle-rich environment. The algorithm is distributed and handles minor faults such as a broken immobile robot and a sudden link failure. In addition, we propose an approach that determines the locations of rendezvous points based on the connected interaction topology and indirectly optimizes the total energy consumption for rendezvous in all robots. Through extensive experiments with the Robotarium multi-robot testbed, we verified and demonstrated the effectiveness of our approach and its properties. |
 | Hierarchical Needs Based Self-Adaptive Framework For Cooperative Multi-Robot System Conference IEEE SMC 2020 INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS, 2020. Abstract | Links | BibTeX | Tags: behavior-trees, multi-robot systems @conference{Yang2020b, title = {Hierarchical Needs Based Self-Adaptive Framework For Cooperative Multi-Robot System}, author = {Qin Yang and Ramviyas Parasuraman}, url = {http://hero.uga.edu/wp-content/uploads/2020/10/SMC_2020___Qin_Yang___Hierarchical_Needs_Based_Multi_Robot_Task_Planning.pdf}, year = {2020}, date = {2020-10-11}, booktitle = {IEEE SMC 2020 INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS}, abstract = {Research in multi-robot and swarm systems has seen significant interest in cooperation of agents in complex and dynamic environments. To effectively adapt to unknown environments and maximize the utility of the group, robots need to cooperate, share information, and make a suitable plan according to the specific scenario. Inspired by Maslow’s hierarchy of human needs and systems theory, we introduce Robot’s Need Hierarchy and propose a new solution called Self-Adaptive Swarm System (SASS). It combines multi-robot perception, communication, planning, and execution with the cooperative management of conflicts through a distributed Negotiation-Agreement Mechanism that prioritizes robot’s needs. We also decompose the complex tasks into simple executable behaviors through several Atomic Operations, such as selection, formation, and routing. We evaluate SASS through simulating static and dynamic tasks and comparing them with the state-of-the-art collision-aware task assignment method integrated into our framework.}, keywords = {behavior-trees, multi-robot systems}, pubstate = {published}, tppubtype = {conference} } Research in multi-robot and swarm systems has seen significant interest in cooperation of agents in complex and dynamic environments. To effectively adapt to unknown environments and maximize the utility of the group, robots need to cooperate, share information, and make a suitable plan according to the specific scenario. Inspired by Maslow’s hierarchy of human needs and systems theory, we introduce Robot’s Need Hierarchy and propose a new solution called Self-Adaptive Swarm System (SASS). It combines multi-robot perception, communication, planning, and execution with the cooperative management of conflicts through a distributed Negotiation-Agreement Mechanism that prioritizes robot’s needs. We also decompose the complex tasks into simple executable behaviors through several Atomic Operations, such as selection, formation, and routing. We evaluate SASS through simulating static and dynamic tasks and comparing them with the state-of-the-art collision-aware task assignment method integrated into our framework. |
2019 |
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 | Self-Reactive Planning of Multi-Robots with Dynamic Task Assignments Conference Int. Symp. on Multi Robot Systems (MRS), Rutgers, NJ, USA 2019. Abstract | BibTeX | Tags: cooperation, multi-robot systems, planning @conference{Yang2019, title = {Self-Reactive Planning of Multi-Robots with Dynamic Task Assignments}, author = {Qin Yang and Zhiwei Luo and Wenzhan Song and Ramviyas Parasuraman}, year = {2019}, date = {2019-08-22}, booktitle = {Int. Symp. on Multi Robot Systems (MRS)}, organization = {Rutgers, NJ, USA}, abstract = {Multi-Robot Systems and Swarms are intelligent systems in which a large number of agents are coordinated in a distributed and decentralized way. Each robot may have homogeneous or heterogeneous capabilities and can be programmed with several fundamental control laws adapting to the environment. Through different kinds of relationships built using the communication protocols, they present various behaviors based on the shared information and current state. To adapt to dynamic environments effectively, and maximize the utility of the group, robots need to cooperate, share their local information, and make a suitable plan according to the specific scenario. In this paper, we formalize the problem of multi-robots fulfilling dynamic tasks using state transitions represented through a Behavior Tree. We design a framework with corresponding distributed algorithms for communications between robots and negotiation and agreement protocols through a novel priority mechanism. Finally, we evaluate our framework through simulation experiments.}, keywords = {cooperation, multi-robot systems, planning}, pubstate = {published}, tppubtype = {conference} } Multi-Robot Systems and Swarms are intelligent systems in which a large number of agents are coordinated in a distributed and decentralized way. Each robot may have homogeneous or heterogeneous capabilities and can be programmed with several fundamental control laws adapting to the environment. Through different kinds of relationships built using the communication protocols, they present various behaviors based on the shared information and current state. To adapt to dynamic environments effectively, and maximize the utility of the group, robots need to cooperate, share their local information, and make a suitable plan according to the specific scenario. In this paper, we formalize the problem of multi-robots fulfilling dynamic tasks using state transitions represented through a Behavior Tree. We design a framework with corresponding distributed algorithms for communications between robots and negotiation and agreement protocols through a novel priority mechanism. Finally, we evaluate our framework through simulation experiments. |
Publications
2026 |
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| SK: Semantic Kernel for Robotic Information Gathering Conference Forthcoming 2026 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Forthcoming. |
| Distributed Connectivity Restoration and Improvement in Networked Robots Conference Forthcoming 2026 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Forthcoming. |
| H-IPP: Altitude-Adaptive Informative Path Planning with Multiple UAVs Conference Forthcoming 2026 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Forthcoming. |
| Multi-Robot Consensus and Coordination with Localization Uncertainty Conference Forthcoming 2026 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Forthcoming. |
| Hybrid Framework for Multi-Robot Target Search in Unknown and Adversarial Environments Conference Forthcoming 2026 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Forthcoming. |
| Multi-Robot Informative Sampling and Coverage in GPS-Denied Environments Conference 2026 IEEE International Conference on Robotics & Automation (ICRA), 2026. |
| DCL-Sparse: Distributed Relative Localization in Sparse Graphs Conference 2026 IEEE International Conference on Robotics & Automation (ICRA), 2026. |
| Energy-Aware Informative Path Planning for Heterogeneous Multi-Robot Systems Conference 2026 IEEE International Conference on Robotics & Automation (ICRA), 2026. |
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. |
| Edge Computing and its Application in Robotics: A Survey Journal Article Journal of Sensor and Actuator Networks, 14 (4), 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. |
| 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. |
| Brief Announcement: Anonymous Distributed Localisation via Spatial Population Protocols Conference 4th Symposium on Algorithmic Foundations of Dynamic Networks (SAND 2025), 2025. |
| 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 |
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| 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. |
| Anchor-oriented Multi-Robot Coverage without Global Localization Workshop IEEE ICRA 2024 Workshop on Sensing and Perception in Extreme Environments (HERMES), 2024, (Spotlight Presentation). |
2023 |
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| Communication-Efficient Reinforcement Learning in Swarm Robotic Networks for Maze Exploration Conference IEEE INFOCOM 2023 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), 2023. |
2022 |
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| DGORL: Distributed Graph Optimization based Relative Localization of Multi-Robot Systems Conference The 16th International Symposium on Distributed Autonomous Robotic Systems (DARS) 2022, 2022, (Nominated for the Best Student Paper Award). |
| Collaborative Control of Mobile Robots Using Analog Twin Framework Workshop ICRA 2022 Workshop on Intelligent Control Methods and Machine Learning Algorithms for Human-Robot Interaction and Assistive Robotics, 2022. |
2020 |
|
| Multi-Point Rendezvous in Multi-Robot Systems Journal Article IEEE Transactions on Cybernetics, 50 (1), pp. 310-323, 2020, ISBN: 2168-2275. |
| Hierarchical Needs Based Self-Adaptive Framework For Cooperative Multi-Robot System Conference IEEE SMC 2020 INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS, 2020. |
2019 |
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| Self-Reactive Planning of Multi-Robots with Dynamic Task Assignments Conference Int. Symp. on Multi Robot Systems (MRS), Rutgers, NJ, USA 2019. |