We present Lifelong Scalable Multi-Agent Realistic Testbed (LSMART), an open-source simulator to evaluate any Multi-Agent Path Finding (MAPF) algorithm in a Fleet Management System (FMS) with Automated Guided Vehicles (AGVs). MAPF aims to move a group of agents from their corresponding starting locations to their goals. Lifelong MAPF (LMAPF) is a variant of MAPF that continuously assigns new goals for agents to reach. LMAPF applications, such as autonomous warehouses, often require a centralized, lifelong system to coordinate the movement of a fleet of robots, typically AGVs. However, existing works on MAPF and LMAPF often assume simplified kinodynamic models, such as pebble motion, as well as perfect execution and communication for AGVs. Prior work has presented SMART, a software capable of evaluating any MAPF algorithms while considering agent kinodynamics, communication delays, and execution uncertainties. However, SMART is designed for MAPF, not LMAPF. Generalizing SMART to an FMS requires many more design choices. First, an FMS parallelizes planning and execution, raising the question of when to plan. Second, given planners with varying optimality and differing agent-model assumptions, one must decide how to plan. Third, when the planner fails to return valid solutions, the system must determine how to recover. In this paper, we first present LSMART, an open-source simulator that incorporates all these considerations to evaluate any MAPF algorithms in an FMS. We then provide experiment results based on state-of-the-art methods for each design choice, offering guidance on how to effectively design centralized lifelong AGV Fleet Management Systems. LSMART is available at https://smart-mapf.github.io/lifelong-smart.

翻译：本文提出了终身可扩展多智能体现实测试平台（LSMART），这是一个开源模拟器，用于在配备自动导引车（AGV）的车队管理系统（FMS）中评估任何多智能体路径规划（MAPF）算法。MAPF的目标是将一组智能体从其对应的起始位置移动到目标位置。终身MAPF（LMAPF）是MAPF的一个变体，它持续为智能体分配新的目标位置。LMAPF的应用场景（如自动化仓库）通常需要一个集中式的终身系统来协调机器人车队（通常是AGV）的移动。然而，现有的MAPF和LMAPF研究通常假设简化的运动动力学模型（如卵石运动模型），以及AGV的完美执行和通信。先前的研究提出了SMART软件，该软件能够在考虑智能体运动动力学、通信延迟和执行不确定性的同时评估任何MAPF算法。然而，SMART是为MAPF设计的，而非LMAPF。将SMART推广到FMS需要处理更多的设计选择。首先，FMS并行化规划与执行，这引发了何时进行规划的问题。其次，面对具有不同最优性和不同智能体模型假设的规划器，必须决定如何进行规划。第三，当规划器未能返回有效解时，系统必须确定如何恢复。在本文中，我们首先介绍了LSMART，这是一个开源模拟器，它整合了所有这些考量，用于在FMS中评估任何MAPF算法。然后，我们基于针对每个设计选择的最先进方法提供了实验结果，为如何有效设计集中式终身AGV车队管理系统提供了指导。LSMART可在 https://smart-mapf.github.io/lifelong-smart 获取。