未知環境におけるマルチロボット動的ソースシーキング
Multi-Robot Dynamical Source Seeking in Unknown Environments
未知の動的環境で複数ロボットが協調して関心源を探索する分散オンラインアルゴリズムDoSSを提案し、D-UCBにより計算量を削減して理論保証とメタン排出探索での有効性を示した。
著者: Bin Du, Kun Qian, Christian Claudel, Dengfeng Sun
分類: cs.RO, cs.SY, eess.SY
原文アブストラクト
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.
