Executing time-sensitive multi-robot missions involves two distinct problems: Multi-Robot Task Assignment (MRTA) and Multi-Agent Path Finding (MAPF). Computing safe paths that complete every task and minimize the time to mission completion, or makespan, is a significant computational challenge even for small teams. In many missions, tasks can be generated during execution which is typically handled by either recomputing task assignments and paths from scratch, or by modifying existing plans using approximate approaches. While performing task reassignment and path finding from scratch produces theoretically optimal results, the computational load makes it too expensive for online implementation. In this work, we present Time-Sensitive Online Task Assignment and Navigation (TSOTAN), a framework which can quickly incorporate online generated tasks while guaranteeing bounded suboptimal task assignment makespans. It does this by assessing the quality of partial task reassignments and only performing a complete reoptimization when the makespan exceeds a user specified suboptimality bound. Through experiments in 2D environments we demonstrate TSOTAN's ability to produce quality solutions with computation times suitable for online implementation.

翻译：执行时间敏感的多机器人任务涉及两个不同的问题：多机器人任务分配（MRTA）与多智能体路径规划（MAPF）。计算能够完成所有任务并最小化任务完成时间（即总完工时间）的安全路径是一项重大的计算挑战，即使对于小规模机器人团队也是如此。在许多任务中，任务可以在执行过程中动态生成，通常的应对方式是重新从头计算任务分配与路径，或者使用近似方法修改现有规划。尽管从头进行任务重新分配与路径规划在理论上能产生最优结果，但其计算负担过重，难以实现在线实施。在本工作中，我们提出了时间敏感在线任务分配与导航（TSOTAN）框架，该框架能快速融入在线生成的任务，同时保证有界次优的任务分配总完工时间。其核心机制是评估部分任务重新分配的质量，仅当总完工时间超过用户指定的次优度界限时，才执行完整的重新优化。通过在二维环境中的实验，我们展示了TSOTAN能够在适合在线实施的计算时间内生成高质量解决方案的能力。