dSLAP: Distributed Safe Learning and Planning for Multi-robot Systems

Zhenyuan Yuan, Minghui Zhu

Research output: Chapter in Book/Report/Conference proceedingConference contribution


This paper considers the problem where a group of mobile robots subject to unknown external disturbances aim to safely reach goal regions. We develop a distributed safe learning and planning algorithm that allows the robots to learn about the external unknown disturbances and safely navigate through the environment via their single trajectories. We use Gaussian process regression for online learning where variance is adopted to quantify the learning uncertainty. By leveraging set-valued analysis, the developed algorithm enables fast adaptation to newly learned models while avoiding collision against the learning uncertainty. Active learning is then applied to return a control policy such that the robots are able to actively explore the unknown disturbances and reach their goal regions in time. Sufficient conditions are established to guarantee the safety of the robots. A set of simulations are conducted for evaluation.

Original languageEnglish (US)
Title of host publication2022 IEEE 61st Conference on Decision and Control, CDC 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781665467612
StatePublished - 2022
Event61st IEEE Conference on Decision and Control, CDC 2022 - Cancun, Mexico
Duration: Dec 6 2022Dec 9 2022

Publication series

NameProceedings of the IEEE Conference on Decision and Control
ISSN (Print)0743-1546
ISSN (Electronic)2576-2370


Conference61st IEEE Conference on Decision and Control, CDC 2022

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Modeling and Simulation
  • Control and Optimization


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