Cooperative multi-robot teams need to be able to explore cluttered and unstructured environments while dealing with communication dropouts that prevent them from exchanging local information to maintain team coordination. Therefore, robots need to consider high-level teammate intentions during action selection. In this letter, we present the first Macro Action Decentralized Exploration Network (MADE-Net) using multi-agent deep reinforcement learning (DRL) to address the challenges of communication dropouts during multi-robot exploration in unseen, unstructured, and cluttered environments. Simulated robot team exploration experiments were conducted and compared against classical and DRL methods where MADE-Net outperformed all benchmark methods in terms of computation time, total travel distance, number of local interactions between robots, and exploration rate across various degrees of communication dropouts. A scalability study in 3D environments showed a decrease in exploration time with MADE-Net with increasing team and environment sizes. The experiments presented highlight the effectiveness and robustness of our method.

翻译：协作多机器人团队需能在杂乱非结构化环境中执行探索任务，同时应对阻碍局部信息交换以维持团队协调的通信中断问题。因此，机器人在动作选择过程中需考虑高层级的队友意图。本文首次提出基于多智能体深度强化学习的宏观动作去中心化探索网络，用于应对未知非结构化杂乱环境中多机器人探索时的通信中断挑战。通过模拟机器人团队探索实验，与经典方法及深度强化学习方法进行对比，该网络在计算时间、总行驶距离、机器人间局部交互次数及不同通信中断程度下的探索速率等方面均优于所有基准方法。三维环境可扩展性研究表明，随着团队及环境规模增大，该网络可缩短探索时间。实验结果验证了该方法的高效性与鲁棒性。