Distributed Allocation and Scheduling of Tasks with Cross-Schedule Dependencies for Heterogeneous Multi-Robot Teams

TL;DR

This paper introduces a distributed, evolutionary-computation-based task allocation and scheduling algorithm for multi-robot systems with cross-schedule dependencies, enabling fast, scalable, and near-optimal online replanning in complex missions.

Contribution

It presents a novel distributed metaheuristic algorithm for task scheduling with temporal and precedence constraints, improving speed and scalability over existing methods.

Findings

Faster computation and better scalability than state-of-the-art methods.

Near-optimal solutions suitable for online replanning.

Successful application to a multi-robot greenhouse maintenance scenario.

Abstract

To enable safe and efficient use of multi-robot systems in everyday life, a robust and fast method for coordinating their actions must be developed. In this paper, we present a distributed task allocation and scheduling algorithm for missions where the tasks of different robots are tightly coupled with temporal and precedence constraints. The approach is based on representing the problem as a variant of the vehicle routing problem, and the solution is found using a distributed metaheuristic algorithm based on evolutionary computation (CBM-pop). Such an approach allows a fast and near-optimal allocation and can therefore be used for online replanning in case of task changes. Simulation results show that the approach has better computational speed and scalability without loss of optimality compared to the state-of-the-art distributed methods. An application of the planning procedure to a practical use case of a greenhouse maintained by a multi-robot system is given.