Multi-Robot Pickup and Delivery via Distributed Resource Allocation

TL;DR

This paper introduces a scalable distributed algorithm for multi-robot pickup and delivery tasks, enabling large networks of robots to coordinate efficiently without central control, demonstrated through simulations and real-world experiments.

Contribution

It presents the first scalable distributed solution for large-scale multi-robot pickup and delivery problems using a primal decomposition approach.

Findings

Algorithm effectively solves large-scale PDVRP instances.

Robots compute only their own solution parts, preserving privacy.

Successful validation through simulations and real robot experiments.

Abstract

In this paper, we consider a large-scale instance of the classical Pickup-and-Delivery Vehicle Routing Problem (PDVRP) that must be solved by a network of mobile cooperating robots. Robots must self-coordinate and self-allocate a set of pickup/delivery tasks while minimizing a given cost figure. This results in a large, challenging Mixed-Integer Linear Problem that must be cooperatively solved % without a central coordinator. We propose a distributed algorithm based on a primal decomposition approach that provides a feasible solution to the problem in finite time. An interesting feature of the proposed scheme is that each robot computes only its own block of solution, thereby preserving privacy of sensible information. The algorithm also exhibits attractive scalability properties that guarantee solvability of the problem even in large networks. To the best of our knowledge, this is the first attempt to provide a scalable distributed solution to the problem. The algorithm is first tested through Gazebo simulations on a ROS~2 platform, highlighting the effectiveness of the proposed solution. Finally, experiments on a real testbed with a team of ground and aerial robots are provided.