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This research introduces a scalable framework for multi-robot coordination that combines offline plan synthesis with online synchronization to handle action delays in real-time. Validated through experiments with nine robots and simulations up to ninety agents, the approach demonstrates reduced computational complexity and enhanced adaptability for complex recurring tasks. The accompanying Python and ROS2 software package facilitates immediate deployment across diverse robotic applications.
Efficient Coordination and Synchronization of Multi-Robot Systems Under Recurring Linear Temporal Logic
arXiv:2502.16531v2 Announce Type: replace Abstract: We consider multi-robot systems under recurring tasks formalized as linear temporal logic (LTL) specifications. To solve the planning problem efficiently, we propose a bottom-up approach combining offline plan synthesis with online coordination, dynamically adjusting plans via real-time communication. To address action delays, we introduce a synchronization mechanism ensuring coordinated task execution, leading to a multi-agent coordination and synchronization framework that is adaptable to a wide range of multi-robot applications. The software package is developed in Python and ROS2 for broad deployment. We validate our findings through lab experiments involving nine robots showing enhanced adaptability compared to previous methods. Additionally, we conduct simulations with up to ninety agents to demonstrate the reduced computational complexity and the scalability features of our work.
Abstract:We consider multi-robot systems under recurring tasks formalized as linear temporal logic (LTL) specifications. To solve the planning problem efficiently, we propose a bottom-up approach combining offline plan synthesis with online coordination, dynamically adjusting plans via real-time communication. To address action delays, we introduce a synchronization mechanism ensuring coordinated task execution, leading to a multi-agent coordination and synchronization framework that is adaptable to a wide range of multi-robot applications. The software package is developed in Python and ROS2 for broad deployment. We validate our findings through lab experiments involving nine robots showing enhanced adaptability compared to previous methods. Additionally, we conduct simulations with up to ninety agents to demonstrate the reduced computational complexity and the scalability features of our work.
Robotics (cs.RO); Systems and Control (eess.SY)
arXiv:2502.16531 [cs.RO]
(or arXiv:2502.16531v2 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2502.16531 Focus to learn more arXiv-issued DOI via DataCite
Proc. IEEE ICRA, 2025, pp. 10194-10200
https://doi.org/10.1109/ICRA55743.2025.11127554 Focus to learn more DOI(s) linking to related resources
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