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，研究者提出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，这些团队需完成用形式语言（如线性时态逻辑，LTL）定义的高层次协作任务。然而，设计出的路径往往缺乏对机器人能力故障（如感知、移动或操作能力）的响应性——这类故障可能由意外事件（如人为干预或系统故障）引发，进而危及任务执行。为应对这一新挑战，本文提出一种针对异构机器人团队执行协作LTL任务的新型韧性任务规划算法。机器人因能力差异呈现异构性，而任务要求这些能力按时间/逻辑顺序在环境特定区域进行应用。所提方法能够设计出适应机器人能力意外故障的路径，通过根据机器人当前正常工作的能力重新分配子任务，同时最小化对现有团队运动计划的干扰。我们通过实验和理论保证证明了该算法的效率与韧性。