Multi-Robot Dynamical Source Seeking in Unknown Environments

TL;DR

This paper introduces 'DoSS', a distributed multi-robot algorithm for real-time source seeking in unknown environments, combining environment estimation and task planning with reduced computational complexity.

Contribution

It proposes a novel D-UCB method that enhances multi-robot source seeking by enabling distributed online operation with theoretical performance guarantees.

Findings

Sub-linear regret bound demonstrated theoretically.

Effective in real-world methane emission seeking.

Reduces computational complexity compared to standard UCB.

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.