We propose a distributed planning method with asynchronous execution for multi-agent pickup and delivery (MAPD) problems for environments with occasional delays in agents' activities and flexible endpoints. MAPD is a crucial problem framework with many applications; however, most existing studies assume ideal agent behaviors and environments, such as a fixed speed of agents, synchronized movements, and a well-designed environment with many short detours for multiple agents to perform tasks easily. However, such an environment is often infeasible; for example, the moving speed of agents may be affected by weather and floor conditions and is often prone to delays. The proposed method can relax some infeasible conditions to apply MAPD in more realistic environments by allowing fluctuated speed in agents' actions and flexible working locations (endpoints). Our experiments showed that our method enables agents to perform MAPD in such an environment efficiently, compared to the baseline methods. We also analyzed the behaviors of agents using our method and discuss the limitations.

翻译：我们提出了一种针对多智能体取送货（MAPD）问题的分布式规划与异步执行方法，适用于智能体活动偶尔出现延迟且端点灵活的环境。MAPD是一个具有广泛应用场景的关键问题框架；然而，现有研究大多假设智能体行为与环境处于理想状态，例如智能体速度恒定、运动同步，以及为多智能体便捷执行任务而设计的多迂回绕道环境。但此类环境在实际中往往难以实现——例如，智能体移动速度可能受天气或地面条件影响而容易发生延迟。本文提出的方法通过允许智能体动作速度波动及灵活的工作位置（端点），可放宽部分不切实际的条件，从而将MAPD应用于更真实的环境。实验表明，与基线方法相比，我们的方法能使智能体在此类环境中高效执行MAPD任务。此外，我们还分析了智能体采用该方法时的行为模式，并讨论了其局限性。