强化学习驱动多机器人探索，通过通信与前沿搜索提升效率。
Reinforcement Learning Driven Multi-Robot Exploration via Explicit Communication and Density-Based Frontier Search
- 基于强化学习的自主决策，结合视野网格与路径特征选择行动。
- 通信受限下仍实现高效探索，减少冗余覆盖，提升全局地图构建速度。
- 适合复杂环境下的多机器人协同任务，尤其适用于救援场景。
协同探索未知环境对搜救任务至关重要。实际应用需应对通信受限及静态与动态障碍物挑战。本文提出一种基于强化学习的去中心化协作框架，提升多智能体在未知环境中的探索能力。各智能体基于自身视角的占用网格和基于A*算法生成的前沿路径特征，自主决策下一步动作。提出一种受约束的通信机制，高效共享环境知识，降低探索冗余。框架去中心化设计使每个智能体独立运行，同时贡献于整体探索目标。在Gymnasium平台的大量仿真与真实世界实验均验证了系统的鲁棒性与有效性，所有结果均表明自主探索与智能体间地图共享结合能显著提升可扩展性与韧性，推动智能机器人探索系统发展。
原文摘要 · Abstract (English)
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.
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