多智能体路径规划（MAPF）问题要求为一组智能体寻找无碰撞路径，引导它们从起点到达目标位置。然而，MAPF并未考虑一些实际的任务相关约束。例如，智能体可能需要在目标位置以特定的执行时间执行动作，并遵守预定的顺序和时间范围。此外，目标分配可能未为智能体预定义，且优化目标可能缺乏明确定义。为了将任务分配、路径规划和用户定义的目标纳入一个连贯的框架中，本文研究了带优先级和时间约束的任务分配与路径规划（TAPF-PTC）问题。我们扩展了Conflict-Based Search（CBS），以同时生成任务分配和无碰撞路径，这些路径遵守优先级和时间约束，并最大化由强化学习（RL）中用户定义奖励函数的回报量化的目标。在实验中，我们证明了我们的算法CBS-TA-PTC能够高效地解决带优先级和时间约束的高难度拆弹任务，其效率相对于MARL和改编的Target Assignment and Path Finding（TAPF）方法表现更优。
英文摘要：
The Multi-Agent Path Finding (MAPF) problem entails finding collision-free paths for a set of agents, guiding them from their start to goal locations. However, MAPF does not account for several practical task-related constraints. For example, agents may need to perform actions at goal locations with specific execution times, adhering to predetermined orders and timeframes. Moreover, goal assignments may not be predefined for agents, and the optimization objective may lack an explicit definition. To incorporate task assignment, path planning, and a user-defined objective into a coherent framework, this paper examines the Task Assignment and Path Finding with Precedence and Temporal Constraints (TAPF-PTC) problem. We augment Conflict-Based Search (CBS) to simultaneously generate task assignments and collision-free paths that adhere to precedence and temporal constraints, maximizing an objective quantified by the return from a user-defined reward function in reinforcement learning (RL). Experimentally, we demonstrate that our algorithm, CBS-TA-PTC, can solve highly challenging bomb-defusing tasks with precedence and temporal constraints efficiently relative to MARL and adapted Target Assignment and Path Finding (TAPF) methods.
