Several task and motion planning algorithms have been proposed recently to design paths for mobile robot teams with collaborative high-level missions specified using formal languages, such as Linear Temporal Logic (LTL). However, the designed paths often lack reactivity to failures of robot capabilities (e.g., sensing, mobility, or manipulation) that can occur due to unanticipated events (e.g., human intervention or system malfunctioning) which in turn may compromise mission performance. To address this novel challenge, in this paper, we propose a new resilient mission planning algorithm for teams of heterogeneous robots with collaborative LTL missions. The robots are heterogeneous with respect to their capabilities while the mission requires applications of these skills at certain areas in the environment in a temporal/logical order. The proposed method designs paths that can adapt to unexpected failures of robot capabilities. This is accomplished by re-allocating sub-tasks to the robots based on their currently functioning skills while minimally disrupting the existing team motion plans. We provide experiments and theoretical guarantees demonstrating the efficiency and resiliency of the proposed algorithm.

翻译：近年来，研究人员提出了多种任务与运动规划算法，用于为移动机器人团队设计路径，这些团队需完成基于形式化语言（如线性时序逻辑LTL）描述的高层协作任务。然而，由于意外事件（如人为干预或系统故障）可能导致机器人能力（如感知、移动或操作能力）失效，这些设计路径往往缺乏对这类故障的响应能力，进而可能影响任务执行性能。针对这一新挑战，本文提出了一种面向异构机器人团队的弹性任务规划算法，其协作任务基于LTL描述。机器人因能力差异而呈现异构性，而任务要求这些能力在环境中的特定区域按时序/逻辑顺序实施。所提方法能够设计出适应机器人能力突发故障的路径，通过根据机器人当前可用能力重新分配子任务，并最小化对现有团队运动计划的干扰来实现。我们通过实验和理论证明展示了该算法的高效性与弹性。