dSLAP: Distributed Safe Learning and Planning for Multi-robot Systems
dSLAP: Distributed Safe Learning and Planning for Multi-robot Systems
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DOI：
10.1109/cdc51059.2022.9992938
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发表时间：
2022-07
期刊：
影响因子：
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通讯作者：
Zhenyuan Yuan;Minghui Zhu
中科院分区：
文献类型：
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作者：
Zhenyuan Yuan;Minghui Zhu
研究了一组移动的机器人在未知外界干扰下安全到达目标区域的问题。我们开发了一种分布式安全学习和规划算法，允许机器人学习外部未知干扰，并通过其单一轨迹安全地在环境中导航。我们使用高斯过程回归进行在线学习，其中采用方差来量化学习的不确定性。通过利用集值分析，所开发的算法能够快速适应新学习的模型，同时避免与学习不确定性的冲突。然后应用主动学习来返回控制策略，使得机器人能够主动探索未知干扰并及时到达目标区域。建立了保证机器人安全的充分条件。一组模拟进行评估。
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.