In this paper, we consider the dynamic multi-robot distribution problem where a heterogeneous group of networked robots is tasked to spread out and simultaneously move towards multiple moving task areas while maintaining connectivity. The heterogeneity of the system is characterized by various categories of units and each robot carries different numbers of units per category representing heterogeneous capabilities. Every task area with different importance demands a total number of units contributed by all of the robots within its area. Moreover, we assume the importance and the total number of units requested from each task area is initially unknown. The robots need first to explore, i.e., reach those areas, and then be allocated to the tasks so to fulfill the requirements. The multi-robot distribution problem is formulated as designing controllers to distribute the robots that maximize the overall task fulfillment while minimizing the traveling costs in presence of connectivity constraints. We propose a novel connectivity-aware multi-robot redistribution approach that accounts for dynamic task allocation and connectivity maintenance for a heterogeneous robot team. Such an approach could generate sub-optimal robot controllers so that the amount of total unfulfilled requirements of the tasks weighted by their importance is minimized and robots stay connected at all times. Simulation and numerical results are provided to demonstrate the effectiveness of the proposed approaches.
翻译:在本文中,我们考虑了一个动态的多机器人分布问题,即由一组网络机器人组成的不同组合负责分散,并同时向多个移动任务区域移动,同时保持连通性。系统的异质性特征特征有不同类别的单位,每个机器人都有不同数量的单位,每个机器人代表不同能力。每个具有不同重要性的任务领域都需要由各自区域内所有机器人提供的总数量单位。此外,我们假定每个任务领域要求的单位的重要性和总数最初并不为人所知。机器人首先需要探索,即进入这些地区,然后分配到多个移动任务领域,以便满足要求。多机器人分布问题被设计成设计控制器,以分配能够最大限度地实现总体任务完成的机器人,同时在连通性受限制的情况下尽量减少旅行费用。我们建议采用新型的连通-觉多机器人再分配办法,以核算一个复杂机器人团队的动态任务分配和连通性维护情况。这样一种方法可以产生亚最佳的机器人控制器,这样可以使按其重要性加权的任务的全部未满足要求的数量达到要求,从而满足要求。多机器人分布的问题被设计成设计成一个控制器,以配置为最小化,同时显示数字和机器人连接所有时间提供的结果。