Task Allocation in Mobile Robot Fleets: A review

TL;DR

This review analyzes current methods for task allocation in mobile robot fleets, emphasizing optimization algorithms including AI approaches, to improve energy efficiency and operational effectiveness in industrial applications.

Contribution

It provides a comprehensive overview of existing task allocation algorithms, including novel AI-based methods, and highlights future research gaps in the field.

Findings

AI-based methods show promising improvements in energy efficiency.

Simulation frameworks are crucial for evaluating task allocation strategies.

Identified gaps suggest need for more adaptive and scalable solutions.

Abstract

Mobile robot fleets are currently used in different scenarios such as medical environments or logistics. The management of these systems provides different challenges that vary from the control of the movement of each robot to the allocation of tasks to be performed. Task Allocation (TA) problem is a key topic for the proper management of mobile robot fleets to ensure the minimization of energy consumption and quantity of necessary robots. Solutions on this aspect are essential to reach economic and environmental sustainability of robot fleets, mainly in industry applications such as warehouse logistics. The minimization of energy consumption introduces TA problem as an optimization issue which has been treated in recent studies. This work focuses on the analysis of current trends in solving TA of mobile robot fleets. Main TA optimization algorithms are presented, including novel methods based on Artificial Intelligence (AI). Additionally, this work showcases most important results extracted from simulations, including frameworks utilized for the development of the simulations. Finally, some conclusions are obtained from the analysis to target on gaps that must be treated in the future.