Multi-Agent Pickup and Delivery (MAPD) is the problem of computing collision-free paths for a group of agents such that they can safely reach delivery locations from pickup ones. These locations are provided at runtime, making MAPD a combination between classical Multi-Agent Path Finding (MAPF) and online task assignment. Current algorithms for MAPD do not consider many of the practical issues encountered in real applications: real agents often do not follow the planned paths perfectly, and may be subject to delays and failures. In this paper, we study the problem of MAPD with delays, and we present two solution approaches that provide robustness guarantees by planning paths that limit the effects of imperfect execution. In particular, we introduce two algorithms, k-TP and p-TP, both based on a decentralized algorithm typically used to solve MAPD, Token Passing (TP), which offer deterministic and probabilistic guarantees, respectively. Experimentally, we compare our algorithms against a version of TP enriched with online replanning. k-TP and p-TP provide robust solutions, significantly reducing the number of replans caused by delays, with little or no increase in solution cost and running time.

翻译：多智能体接送（MAPD）问题是指计算一组智能体从取货点安全到达送货点的无碰撞路径。这些位置在运行时动态提供，使得MAPD成为经典多智能体路径规划（MAPF）与在线任务分配的结合。现有MAPD算法未考虑实际应用中遇到的诸多问题：真实智能体往往无法完美遵循规划路径，可能遭遇延迟与故障。本文研究带延迟的MAPD问题，提出两种通过规划路径限制不完美执行影响的鲁棒性保障方法。具体而言，我们提出两种算法k-TP与p-TP，两者均基于常用于求解MAPD的分布式算法Token Passing（TP），分别提供确定性与概率性保障。实验中将所提算法与加入在线重规划的增强版TP进行对比。k-TP与p-TP能提供鲁棒解，显著减少因延迟引发的重规划次数，且解成本与运行时间几乎不增加。