用深度强化学习让多机器人高效发现未知环境中的目标
Scalable Multi-Robot Informative Path Planning for Target Mapping via Deep Reinforcement Learning
- 用统一策略网络实现多机器人协同探索，通过图机制优先访问未探测区域
- 在64个机器人的复杂环境中，发现目标数比现有方法高出26.2%以上
- 每步规划耗时低于2秒，适合实时部署于大规模未知3D环境
自主机器人因成本低被广泛用于测绘与探索任务。多机器人系统在复杂环境中具有可扩展性和高效率优势。这类任务属于多机器人信息路径规划（MRIPP）问题。本文提出一种基于深度强化学习的MRIPP方法，旨在未知3D环境中最大化静态目标的发现数量，同时满足路径长度等资源约束。每个机器人需在通信与资源受限条件下，最大化发现目标、避开静态障碍物并避免彼此碰撞。采用集中训练、分散执行范式，训练单一策略神经网络。关键创新在于协调图机制，优先选择其他机器人尚未探索的区域。所学策略可复制至任意数量机器人，在训练中未见的复杂环境中部署。实验表明，该方法在发现目标数量上优于当前最优方法至少26.2%，每步规划时间少于2秒。结果涵盖最多64个机器人的复杂场景，并与基线规划器比较成功率。代码与训练模型已开源。
原文摘要 · Abstract (English)
Autonomous robots are widely utilized for mapping and exploration tasks due to their cost-effectiveness. Multi-robot systems offer scalability and efficiency, especially in terms of the number of robots deployed in more complex environments. These tasks belong to the set of Multi-Robot Informative Path Planning (MRIPP) problems. In this paper, we propose a deep reinforcement learning approach for the MRIPP problem. We aim to maximize the number of discovered stationary targets in an unknown 3D environment while operating under resource constraints (such as path length). Here, each robot aims to maximize discovered targets, avoid unknown static obstacles, and prevent inter-robot collisions while operating under communication and resource constraints. We utilize the centralized training and decentralized execution paradigm to train a single policy neural network. A key aspect of our approach is our coordination graph that prioritizes visiting regions not yet explored by other robots. Our learned policy can be copied onto any number of robots for deployment in more complex environments not seen during training. Our approach outperforms state-of-the-art approaches by at least 26.2% in terms of the number of discovered targets while requiring a planning time of less than 2 sec per step. We present results for more complex environments with up to 64 robots and compare success rates against baseline planners. Our code and trained model are available at - https://github.com/AccGen99/marl_ipp
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