面向迷宫探索的群体机器人网络中通信高效的强化学习
Communication-Efficient Reinforcement Learning in Swarm Robotic Networks for Maze Exploration
AI总结：
本文提出一种通信高效的去中心化协作强化学习算法，通过分层局部信息交换协调群体机器人进行迷宫探索，在通信退化环境下显著提升覆盖精度与效率并降低成本。
摘要：
群体机器人系统内的顺畅协调对于有效执行集体机器人任务至关重要。拥有高效的通信是群体机器人成功协调的关键。本文提出了一种新的通信高效的去中心化协作强化学习算法，用于协调群体机器人。该算法通过分层构建在局部信息交换的使用之上而实现高效。我们考虑了一个通过一组机器人协作进行迷宫求解的案例研究应用，其中在探索过程中最小化时间和成本，同时避免机器人间碰撞和路径重叠。基于坚实的理论基础，我们利用真实的CORE网络仿真对该算法进行了广泛分析，并在通信退化环境下，就迷宫覆盖百分比和效率方面与最先进的解决方案进行了评估。结果表明，即使在高丢包率和低通信范围场景下，该算法也能显著提高覆盖精度和效率，同时降低成本和重叠。
英文摘要：
Smooth coordination within a swarm robotic system is essential for the effective execution of collective robot missions. Having efficient communication is key to the successful coordination of swarm robots. This paper proposes a new communication-efficient decentralized cooperative reinforcement learning algorithm for coordinating swarm robots. It is made efficient by hierarchically building on the use of local information exchanges. We consider a case study application of maze solving through cooperation among a group of robots, where the time and costs are minimized while avoiding inter-robot collisions and path overlaps during exploration. With a solid theoretical basis, we extensively analyze the algorithm with realistic CORE network simulations and evaluate it against state-of-the-art solutions in terms of maze coverage percentage and efficiency under communication-degraded environments. The results demonstrate significantly higher coverage accuracy and efficiency while reducing costs and overlaps even in high packet loss and low communication range scenarios.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
