Multi-Robot Dynamical Source Seeking in Unknown Environments

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.

I. Introduction

Over the past decades, source seeking has been a fundamentally crucial problem and attracted increasing attentions, due to its various applications including surveillance [1], [2], environment and health monitoring [3]–[6], to name a few. Source seeking involves locating one or several positions, associated with measurements maxima, in a possibly unknown and noisy environment. In this paper, we are particularly interested in solving the problem of source seeking with a multi-robot system, in which a team of robots are deployed and expected to cooperatively locate as many local maxima sources as possible, by leveraging the communications among different robots. In addition, we consider that the environment is not only unknown but also changing dynamically as the robots acquire knowledge from it. Under this circumstance, the team of robots needs to track the moving sources in real-time. We remark that these two settings, i.e., the multi-robot system and dynamical environment, make our problem significantly challenging to solve.