強化学習と明示的通信・密度ベースフロンティア探索によるマルチロボット探査
Reinforcement Learning Driven Multi-Robot Exploration via Explicit Communication and Density-Based Frontier Search
未知環境でのマルチエージェント探査を強化学習で行い、限られた通信で地図情報を共有しながらフロンティア探索を効率化する分散協調フレームワークを提案した。
著者: Gabriele Calzolari, Vidya Sumathy, Christoforos Kanellakis, George Nikolakopoulos
分類: cs.RO
原文アブストラクト
Collaborative multi-agent exploration of unknown environments is crucial for search and rescue operations. Effective real-world deployment must address challenges such as limited inter-agent communication and static and dynamic obstacles. This paper introduces a novel decentralized collaborative framework based on Reinforcement Learning to enhance multi-agent exploration in unknown environments. Our approach enables agents to decide their next action using an agent-centered field-of-view occupancy grid, and features extracted from $\text{A}^*$ algorithm-based trajectories to frontiers in the reconstructed global map. Furthermore, we propose a constrained communication scheme that enables agents to share their environmental knowledge efficiently, minimizing exploration redundancy. The decentralized nature of our framework ensures that each agent operates autonomously, while contributing to a collective exploration mission. Extensive simulations in Gymnasium and real-world experiments demonstrate the robustness and effectiveness of our system, while all the results highlight the benefits of combining autonomous exploration with inter-agent map sharing, advancing the development of scalable and resilient robotic exploration systems.
