Decentralized Coordination for Multi-Agent Data Collection in Dynamic Environments

Nhat Nguyen

doi:10.1109/TMC.2024.3437360

Decentralized Coordination for Multi-Agent Data Collection in Dynamic Environments

2024-12-01

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DOI：10.1109/TMC.2024.3437360

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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the exploration-exploitation trade-off to deal effectively with changes in the environment while filtering out outdated and irrelevant samples via a sliding window mechanism.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.

Keywords:

Planning

Vehicle dynamics

Heuristic algorithms

Wireless sensor networks

Monitoring

Data collection

Active perception

autonomous underwater vehicles

Monte-Carlo tree search (MCTS)

multi-agent systems

underwater sensor networks

Decentralized Coordination for Multi-Agent Data Collection in Dynamic Environments

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