Heterogeneous multi-robot systems are advantageous for operations in unknown environments because functionally specialised robots can gather environmental information, while others perform tasks. We define this decomposition as the scout-task robot architecture and show how it avoids the need to explicitly balance exploration and exploitation~by permitting the system to do both simultaneously. The challenge is to guide exploration in a way that improves overall performance for time-limited tasks. We derive a novel upper confidence bound for simultaneous exploration and exploitation based on mutual information and present a general solution for scout-task coordination using decentralised Monte Carlo tree search. We evaluate the performance of our algorithms in a multi-drone surveillance scenario in which scout robots are equipped with low-resolution, long-range sensors and task robots capture detailed information using short-range sensors. The results address a new class of coordination problem for heterogeneous teams that has many practical applications.
翻译:由于功能专用的机器人可以收集环境信息,而其他机器人则可以执行任务,因此,多色机器人系统在未知环境中的运作是有利的,因为功能专用机器人可以收集环境信息,而其他机器人则可以执行任务。我们把这种分解定义为童子星机器人结构,并表明它如何避免需要明确平衡勘探和开发之间的平衡,同时允许该系统同时进行。我们面临的挑战是如何指导勘探,提高有时限的任务的总体性能。我们基于相互信息获得一个新的上层信任,可以同时进行勘探和开发,并且提出利用分散的蒙特卡洛树搜索进行童子-任务协调的一般解决办法。我们评估了我们的算法在多河监测情景中的性能,在这种情景中,童子机器人配备了低分辨率、长程传感器和任务机器人,利用短程传感器获取详细信息。结果解决了具有许多实际应用的多变种团队的新型协调问题。