Frontier exploration and reinforcement learning have historically been used to solve the problem of enabling many mobile robots to autonomously and cooperatively explore complex surroundings. These methods need to keep an internal global map for navigation, but they do not take into consideration the high costs of communication and information sharing between robots. This study offers CQLite, a novel distributed Q-learning technique designed to minimize data communication overhead between robots while achieving rapid convergence and thorough coverage in multi-robot exploration. The proposed CQLite method uses ad hoc map merging, and selectively shares updated Q-values at recently identified frontiers to significantly reduce communication costs. The theoretical analysis of CQLite's convergence and efficiency, together with extensive numerical verification on simulated indoor maps utilizing several robots, demonstrates the method's novelty. With over 2x reductions in computation and communication alongside improved mapping performance, CQLite outperformed cutting-edge multi-robot exploration techniques like Rapidly Exploring Random Trees and Deep Reinforcement Learning.

翻译：前沿探索与强化学习历来被用于解决多台移动机器人自主协同探索复杂环境的问题。这些方法需要维护内部全局地图进行导航，但未考虑机器人间通信与信息共享的高昂成本。本研究提出CQLite——一种新型分布式Q学习技术，旨在多机器人探索中最小化数据通信开销的同时实现快速收敛与全面覆盖。所提CQLite方法采用自组织地图合并策略，并选择性地共享最近识别前沿的更新Q值，从而显著降低通信成本。结合多机器人仿真室内地图的广泛数值验证，对CQLite收敛性与效率的理论分析证明了该方法的新颖性。相比快速随机扩展树与深度强化学习等前沿多机器人探索技术，CQLite在计算与通信方面实现了两倍以上的降低，同时提升了建图性能。