Task Allocation Algorithms for Mobile Robots, Application to Industrial Logistics

This publication explores task allocation algorithms for mobile robots in industrial logistics. It examines both centralised and decentralised control methods, according to their flexibility, robustness, and scalability. The study focuses on optimising task allocation in a multi-robot system using different strategies such as linear programming, iterative calculation, conflict resolution by consensus, machine learning models, and reinforcement learning. The proposed methods are evaluated through simulations in an industrial setting, specifically within an aircraft engine nacelle assembly company. The goal is to enhance efficiency by allowing robots to autonomously assign tasks based on their capabilities and constraints. The research concludes with a comparison of the algorithms' performance, providing insights for future applications in industrial automation.