Decentralized Coordination for Multi-Agent Data Collection in Dynamic Environments

Coordinated multi-robot systems are an effective way to harvest data from sensor networks and implement active perception strategies. However, achieving efficient coordination in a way that guarantees a target QoS while adapting dynamically to changes (in the environment and/or in the system) is a key open issue. In this paper, we propose a novel decentralized Monte Carlo Tree Search (MCTS) algorithm for dynamic environments that allows agents to optimize their own actions while achieving some form of coordination. Its main underlying idea is to balance adaptively the exploration-exploitation trade-off to deal effectively with changes in the environment while filtering out outdated and irrelevant samples via a sliding window mechanism. We show both theoretically and through simulations that in dynamic environments our algorithm provides a log-factor (in terms of time steps) smaller regret than state-of-the-art decentralized multi-agent planning methods. We instantiate our approach to the problem of underwater data collection, showing in a variety of different settings that our approach greatly outperforms the best-competing approaches, both in terms of convergence speed and global utility.