综述移动机器人集群任务分配研究，聚焦节能与效率优化。
Task Allocation in Mobile Robot Fleets: A review
- 梳理主流任务分配算法，涵盖人工智能新方法
- 总结仿真结果，验证算法在节能与资源减少上的效果
- 适合机器人调度与智能物流系统研究者参考
移动机器人集群已广泛应用于医疗、物流等场景。其管理面临从单体控制到任务分配等一系列挑战。任务分配（TA）是确保机器人集群高效运行的关键，有助于降低能耗并减少所需机器人数量。近年来，该问题被建模为优化问题，推动了多种解决方案的发展。本文综述当前任务分配研究趋势，介绍主要优化算法，包括基于人工智能的新方法。同时展示关键仿真结果及所用仿真框架，并基于分析提出未来需解决的研究空白。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。