提出一套监控与优化多机器人路径执行的完整架构，动态判断何时重规划以缩短任务时长。
A Holistic Architecture for Monitoring and Optimization of Robust Multi-Agent Path Finding Plan Execution
- 用动作依赖图实时估算执行时长，评估重规划收益
- 实验表明可显著降低因延迟累积导致的总执行时间
- 适合需高鲁棒性与效率的仓储机器人系统
多智能体路径规划（MAPF）的目标是在共享环境中为一组智能体规划路径，使其无碰撞地到达目标。实际执行中，部分机器人可能产生延迟，增加碰撞风险。尽管采用鲁棒执行方法确保安全，延迟仍会显著影响任务总时长。当延迟累积到一定程度时，即使原计划最优，也可能存在新计划能更早完成。但重规划代价高昂，关键问题是何时触发。本文提出一种用于稳健执行、监控与优化的完整架构。利用动作依赖图（Action Dependency Graph）在执行过程中持续估算预期时长，并据此预测重规划是否能带来更短执行时间。我们在自研的实时仿真器中进行实验，该仿真器模拟真实仓库中的自主机器人车队演示场景。
原文摘要 · Abstract (English)
The goal of Multi-Agent Path Finding (MAPF) is to find a set of paths for a fleet of agents moving in a shared environment such that the agents reach their goals without colliding with each other. In practice, some of the robots executing the plan may get delayed, which can introduce collision risk. Although robust execution methods are used to ensure safety even in the presence of delays, the delays may still have a significant impact on the duration of the execution. At some point, the accumulated delays may become significant enough that instead of continuing with the execution of the original plan, even if it was optimal, there may now exist an alternate plan which will lead to a shorter execution. However, the problem is how to decide when to search for the alternate plan, since it is a costly procedure. In this paper, we propose a holistic architecture for robust execution of MAPF plans, its monitoring and optimization. We exploit a robust execution method called Action Dependency Graph to maintain an estimate of the expected execution duration during the plan's execution. This estimate is used to predict the potential that finding an alternate plan would lead to shorter execution. We empirically evaluate the architecture in experiments in a real-time simulator which we designed to mimic our real-life demonstrator of an autonomous warehouse robotic fleet.
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