Multi-Robot Dynamical Source Seeking in Unknown Environments
Multi-Robot Dynamical Source Seeking in Unknown Environments
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DOI：
10.1109/icra48506.2021.9561014
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发表时间：
2021-05
期刊：
影响因子：
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通讯作者：
Bin Du;Kun Qian;C. Claudel;Dengfeng Sun
中科院分区：
文献类型：
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作者：
Bin Du;Kun Qian;C. Claudel;Dengfeng Sun
本文提出了一个多机器人系统在未知动态环境下的分布式在线源搜索算法框架（DoSS）。我们的算法建立在一个称为虚拟置信上限（D-UCB）的新概念之上，同时集成了对未知环境的估计和多个机器人的任务规划，从而将机器人团队驱动到一个稳定的状态，其中多个兴趣源位于其中。与多臂强盗环境下的标准UCB算法不同，D-UCB的引入显著降低了求解多机器人任务规划子问题的计算复杂度。这也使得我们的DoSS算法能够以分布式在线的方式实现。通过给出累积遗憾的次线性上界，从理论上保证了算法的性能。最后给出了实际甲烷排放求解问题的数值结果，验证了该算法的有效性。
This paper presents an algorithmic framework for the distributed on-line source seeking, termed as DoSS, with a multi-robot system in an unknown dynamical environment. Our algorithm, building on a novel concept called dummy confidence upper bound (D-UCB), integrates both estimation of the unknown environment and task planning for the multiple robots simultaneously, and as a result, drives the team of robots to a steady state in which multiple sources of interest are located. Unlike the standard UCB algorithm in the context of multi-armed bandits, the introduction of D-UCB significantly reduces the computational complexity in solving subproblems of the multi-robot task planning. This also enables our DoSS algorithm to be implementable in a distributed on-line manner. The performance of the algorithm is theoretically guaranteed by showing a sub-linear upper bound of the cumulative regret. Numerical results on a real-world methane emission seeking problem are also provided to demonstrate the effectiveness of the proposed algorithm.