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

TL;DR

This paper introduces a coupled task assignment and path planning method for multi-agent pickup and delivery, improving efficiency by using actual delivery costs and handling multiple tasks per robot.

Contribution

It presents a novel integrated approach that informs task assignment with real delivery costs and extends to robots carrying multiple tasks, outperforming existing methods.

Findings

Significant improvement over recent methods in solution efficiency.

Effective handling of multiple tasks per robot.

Use of Large Neighbourhood Search enhances solution quality.

Abstract

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.