Integrated Task Assignment and Path Planning for Capacitated Multi-Agent Pickup and Delivery

Multi-agent Pickup and Delivery (MAPD) is a challenging industrial problem where a team of robots is tasked with transporting a set of tasks, each from an initial location and each to a specified target location. Appearing in the context of automated warehouse logistics and automated mail sortation, MAPD requires first deciding which robot is assigned what task (i.e., Task Assignment or TA) followed by a subsequent coordination problem where each robot must be assigned collision-free paths so as to successfully complete its assignment (i.e., Multi-Agent Path Finding or MAPF). Leading methods in this area solve MAPD sequentially: first assigning tasks, then assigning paths. In this work we propose a new coupled method where task assignment choices are informed by actual delivery costs instead of by lower-bound estimates. The main ingredients of our approach are a marginal-cost assignment heuristic and a meta-heuristic improvement strategy based on Large Neighbourhood Search. As a further contribution, we also consider a variant of the MAPD problem where each robot can carry multiple tasks instead of just one. Numerical simulations show that our approach yields efficient and timely solutions and we report significant improvement compared with other recent methods from the literature.

I. Introduction

In automated warehouse systems, a team of robots works together to fulfill a set of customer orders. Each order comprises one or more items found on the warehouse floor, which must be delivered to a picking station for consolidation and delivery. In automated sortation centres, meanwhile, a similar problem arises. Here, the robotic team is tasked with carrying mail tasks from one of several emitter stations, where new parcels arrive, to a bin of sorted tasks, all bound for the same processing facility where they will be dispatched for delivery. Illustrated in Fig. 1, such systems are at the heart of logistics operations for major online retailers such as Amazon and Alibaba. Practical success in both of these contexts depends on computing timely solutions to a challenging optimization problem known in the literature as Multi-agent Pickup and Delivery (MAPD) [1].