Distributed Safe Learning and Planning for Multi-Robot Systems

This paper considers the problem of online multi-robot motion planning with general nonlinear dynamics subject to unknown external disturbances. We propose dSLAP, a d istributed S afe L earning A nd P lanning framework that allows the robots to safely navigate through the environments by coupling on...

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Bibliographic Details
Published inIEEE transactions on automatic control pp. 1 - 16
Main Authors Yuan, Zhenyuan, Zhu, Minghui
Format Journal Article
LanguageEnglish
Published IEEE 2025
Subjects
Collision avoidance
Control systems
Dynamics
Finite element analysis
Multi-robot systems
Planning
Robot kinematics
Robots
Safety
Uncertainty
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Summary:This paper considers the problem of online multi-robot motion planning with general nonlinear dynamics subject to unknown external disturbances. We propose dSLAP, a d istributed S afe L earning A nd P lanning framework that allows the robots to safely navigate through the environments by coupling online learning and motion planning. Gaussian process regression is used to online learn the disturbances with uncertainty quantification. The planning algorithm ensures collision avoidance against the learning uncertainty and utilizes set-valued analysis to achieve fast adaptation in response to the newly learned models. A set-valued model predictive control problem is formulated and solved to return a control policy that balances between actively exploring the unknown disturbances and reaching goal regions. Sufficient conditions are established to guarantee the safety of the robots. Monte Carlo simulations are conducted for evaluation.
ISSN:0018-9286
1558-2523
DOI:10.1109/TAC.2025.3576037