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.

翻译：群体机器人系统中流畅的协调对于有效执行集体机器人任务至关重要。高效的通信是实现群体机器人成功协调的关键。本文提出一种新的面向群体机器人协调的通信高效去中心化协同强化学习算法。该算法通过分层利用局部信息交换实现高效性。我们以一组机器人通过协作解决迷宫问题作为案例研究，在探索过程中最小化时间和成本，同时避免机器人间碰撞与路径重叠。基于扎实的理论基础，我们通过真实的CORE网络仿真对算法进行广泛分析，并在通信降级环境中与现有最优解决方案进行迷宫覆盖率和效率对比评估。结果表明，即使在高丢包率和低通信范围场景下，该算法仍能显著提升覆盖精度与效率，同时降低成本和重叠。