This paper proposes a cooperative environmental learning algorithm working in a fully distributed manner. A multi-robot system is more effective for exploration tasks than a single robot, but it involves the following challenges: 1) online distributed learning of environmental map using multiple robots; 2) generation of safe and efficient exploration path based on the learned map; and 3) maintenance of the scalability with respect to the number of robots. To this end, we divide the entire process into two stages of environmental learning and path planning. Distributed algorithms are applied in each stage and combined through communication between adjacent robots. The environmental learning algorithm uses a distributed Gaussian process, and the path planning algorithm uses a distributed Monte Carlo tree search. As a result, we build a scalable system without the constraint on the number of robots. Simulation results demonstrate the performance and scalability of the proposed system. Moreover, a real-world-dataset-based simulation validates the utility of our algorithm in a more realistic scenario.
翻译:本文建议了一种合作性的环境学习算法,以完全分布的方式运作。多机器人系统比一个机器人对勘探任务更有效,但它涉及以下挑战:(1) 使用多个机器人在线分布环境地图学习;(2) 根据所学的地图生成安全和高效的勘探路径;(3) 维护机器人数量的可伸缩性。为此,我们将整个过程分为两个环境学习和路径规划阶段。每个阶段都应用分布式算法,并通过相邻机器人之间的交流加以合并。环境学习算法使用分布式高斯进程,路径规划算法使用分布式蒙特卡洛树搜索。结果,我们建立一个可伸缩的系统,不受机器人数目的限制。模拟结果显示了拟议系统的性能和可伸缩性。此外,基于真实世界数据集的模拟验证了我们算法在更现实的情景中的效用。