An Upper Confidence Bound for Simultaneous Exploration and Exploitation in Heterogeneous Multi-Robot Systems
This addresses a coordination problem for heterogeneous robot teams in unknown environments, with practical applications in surveillance and similar domains, but it is incremental as it builds on existing exploration-exploitation and multi-robot methods.
The paper tackles the challenge of coordinating heterogeneous multi-robot systems for time-limited tasks by proposing a scout-task architecture that allows simultaneous exploration and exploitation, deriving a novel upper confidence bound based on mutual information and using decentralized Monte Carlo tree search, with evaluation in a multi-drone surveillance scenario showing improved performance.
Heterogeneous multi-robot systems are advantageous for operations in unknown environments because functionally specialised robots can gather environmental information, while others perform tasks. We define this decomposition as the scout-task robot architecture and show how it avoids the need to explicitly balance exploration and exploitation~by permitting the system to do both simultaneously. The challenge is to guide exploration in a way that improves overall performance for time-limited tasks. We derive a novel upper confidence bound for simultaneous exploration and exploitation based on mutual information and present a general solution for scout-task coordination using decentralised Monte Carlo tree search. We evaluate the performance of our algorithms in a multi-drone surveillance scenario in which scout robots are equipped with low-resolution, long-range sensors and task robots capture detailed information using short-range sensors. The results address a new class of coordination problem for heterogeneous teams that has many practical applications.