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arXiv · 2103.11016

Multi-Robot Dynamical Source Seeking in Unknown Environments

Bin Du·Kun Qian·Christian Claudel·Dengfeng Sun
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Abstract

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.

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Bin Du, Kun Qian, Christian Claudel, Dengfeng Sun. 2021-03-19. Multi-Robot Dynamical Source Seeking in Unknown Environments. https://arxiv.org/abs/2103.11016

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