🤖 AI Summary
Task allocation (TA) for mobile robot swarms in hospital, logistics, and warehouse environments demands simultaneous optimization of energy consumption and robot count to enhance economic viability and environmental sustainability.
Method: We conduct a systematic review of AI-driven TA approaches and propose the first sustainability-oriented multidimensional evaluation framework—covering centralized/distributed optimization, reinforcement learning, and multi-agent game-theoretic methods—validated via simulations in ROS, MATLAB/Simulink, and ARGoS. Performance is quantitatively benchmarked across energy consumption, task completion time, and scalability.
Contribution/Results: We introduce the first TA evaluation framework explicitly targeting energy efficiency and resource minimization. Our analysis identifies critical research gaps and provides actionable algorithm selection guidelines. The framework delivers both theoretical foundations and practical implementation pathways for green, cost-effective deployment of mobile robots in industrial logistics.
📝 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.