@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}
}

@conference{Latif2022b,
title = {Multi-Robot Synergistic Localization in Dynamic Environments},
author = {Ehsan Latif and Ramviyas Parasuraman},
url = {Preprint: https://arxiv.org/pdf/2206.03573.pdf
Paper: https://ieeexplore.ieee.org/abstract/document/9861805},
year = {2022},
date = {2022-06-21},
booktitle = {ISR Europe 2022; 54th International Symposium on Robotics},
pages = {109-116},
abstract = {A mobile robot’s precise location information is critical for navigation and task processing, especially for a multi-robot system (MRS) to collaborate and collect valuable data from the field. However, a robot in situations where it does not have access to GPS signals, such as in an environmentally controlled, indoor, or underground environment, finds it difficult to locate using its sensor alone. As a result, robots sharing their local information to improve their localization estimates benefit the entire MRS team. There have been several attempts to model-based multi-robot localization using Radio Signal Strength Indicator (RSSI) as a source to calculate bearing information. We also utilize the RSSI for wireless networks generated through the communication of multiple robots in a system and aim to localize agents with high accuracy and efficiency in a dynamic environment for shared information fusion to refine the localization estimation. This estimator structure reduces one source of measurement correlation while appropriately incorporating others. This paper proposes a decentralized Multi-robot Synergistic Localization System (MRSL) for a dense and dynamic environment. Robots update their position estimation whenever new information receives from their neighbors. When the system senses the presence of other robots in the region, it exchanges position estimates and merges the received data to improve its localization accuracy. Our approach uses Bayesian rule-based integration, which has shown to be computationally efficient and applicable to asynchronous robotics communication. We have performed extensive simulation experiments with a varying number of robots to analyze the algorithm. MRSL’s localization accuracy with RSSI outperformed others on any number of robots, 66% higher than autonomous robot localization (ARL) (which works without collaboration between robots) and 32% higher than the collaborative multi-robot algorithm from the literature. Nevertheless, simulation results have shown significant promise in localization accuracy for many collaborating robots in a dynamic environment.},
keywords = {localization, multi-robot-systems},
pubstate = {published},
tppubtype = {conference}
}

@conference{Yang2022,
title = {Game-theoretic Utility Tree for Multi-Robot Cooperative Pursuit Strategy},
author = {Qin Yang and Ramviyas Parasuraman},
url = {Preprint: https://arxiv.org/pdf/2206.01109.pdf
Paper: https://ieeexplore.ieee.org/abstract/document/9861828
Codes: https://github.com/herolab-uga/gut-pursuit-evasion-robotarium},
year = {2022},
date = {2022-06-21},
booktitle = {ISR Europe 2022; 54th International Symposium on Robotics
},
pages = {278-284},
abstract = {Underlying relationships among multiagent systems (MAS) in hazardous scenarios can be represented as game-theoretic models. In adversarial environments, the adversaries can be intentional or unintentional based on their needs and motivations. Agents will adopt suitable decision-making strategies to maximize their current needs and minimize their expected costs. This paper proposes and extends the new hierarchical network-based model, termed Game-theoretic Utility Tree (GUT), to arrive at a cooperative pursuit strategy to catch an evader in the Pursuit-Evasion game domain. We verify and demonstrate the performance of the proposed method using the Robotarium platform compared to the conventional constant bearing (CB) and pure pursuit (PP) strategies. The experiments demonstrated the effectiveness of the GUT, and the performances validated that the GUT could effectively organize cooperation strategies, helping the group with fewer advantages achieve higher performance.},
keywords = {control, multi-robot-systems, multiagent-systems, planning},
pubstate = {published},
tppubtype = {conference}
}

@conference{Yang2020b,
title = {Needs-driven Heterogeneous Multi-Robot Cooperation in Rescue Missions},
author = {Qin Yang and Ramviyas Parasuraman},
url = {https://arxiv.org/abs/2009.00288},
year = {2020},
date = {2020-11-06},
booktitle = {2020 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR 2020)},
abstract = {This paper focuses on the teaming aspects and the role of heterogeneity in a multi-robot system applied to robot-aided urban search and rescue (USAR) missions. We specifically propose a needs-driven multi-robot cooperation mechanism represented through a Behavior Tree structure and evaluate the performance of the system in terms of the group utility and energy cost to achieve the rescue mission
in a limited time. From the theoretical analysis, we prove that the needs-drive cooperation in a heterogeneous robot system enables higher group utility compared to a homogeneous robot system. We also perform simulation experiments to verify the proposed needs-driven cooperation and show that the heterogeneous multi-robot cooperation can achieve better performance and increase system robustness
by reducing uncertainty in task execution. Finally, we discuss the application to human-robot teaming.},
keywords = {human-robot interaction, multi-robot-systems, robotics},
pubstate = {published},
tppubtype = {conference}
}

@article{Luo2019,
title = {Multi-robot Rendezvous Based on Bearing-aided Hierarchical Tracking of Network Topology},
author = {Shaocheng Luo,
Jonghoek Kim,
Ramviyas Parasuraman,
Jun Han Bae, Eric T Matson,
Byung-Cheol Min},
url = {https://www.sciencedirect.com/science/article/pii/S1570870518301100},
doi = {10.1016/j.adhoc.2018.11.004},
year = {2019},
date = {2019-04-01},
journal = {Ad hoc Networks},
volume = {86},
pages = {131-143},
abstract = {Rendezvous control is an important module of a multi-robot system to enable formation control of multiple robots without losing network connectivity. This paper introduces a new coordinate-free, bearing-only algorithm, based on hierarchical tracking of wireless network topology, to enable rendezvous of distributed mobile robots at any designated leader robot node. An assumption is made that the robot can only detect and communicate with their neighbors (i.e., local sensing). The proposed approach preserves connectivity during the rendezvous task, adapts to dynamic changes in the network topology (e.g., losing or re-gaining a communication link), and is tolerant of mobility faults in the robots. We theoretically analyze the proposed algorithm and experimentally demonstrate the approach through simulations and extensive field experiments. The results indicate that the method is effective in a variety of realistic scenarios in which the robots are distributed in a cluttered environment.
},
keywords = {control, multi-robot-systems},
pubstate = {published},
tppubtype = {article}
}