Multi-Robot Dynamical Source Seeking in Unknown Environments
This addresses the challenge of efficient distributed source localization for robotics applications, representing an incremental improvement over existing multi-armed bandit methods.
The paper tackles the problem of multi-robot source seeking in unknown dynamic environments by proposing the DoSS algorithm, which uses a dummy confidence upper bound to reduce computational complexity and achieves a sub-linear cumulative regret bound with demonstrated effectiveness in a methane emission seeking scenario.
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.