均值漂移理论及其在群体机器人中的应用：一种提升多机器人协作效率的新途径
Mean-Shift Theory and Its Applications in Swarm Robotics: A New Way to Enhance the Efficiency of Multi-Robot Collaboration
AI总结：
本文综述了群体机器人无分配协作的最新进展，重点介绍均值漂移探索策略如何将大规模群体形状形成效率提升数十倍，并展示其在智能仓储、区域探索和货物运输中的应用。
摘要：
由多个个体之间的集体行为演化而成的群体在自然界中十分常见，这使得生物系统能够展现出更高效、更鲁棒的协作能力。在工程机器人中创造类似的群体智能，对可大规模编程的协作算法设计提出了挑战。基于分配的方法在很长一段时间内一直在解决机器人群体协作问题中扮演着重要角色。然而，由于其对群体变体缺乏可扩展性，该方法在效率和鲁棒性方面面临根本性局限。本文对机器人群体无分配协作的最新进展进行了教程式综述，重点关注形状形成问题。其中一个关键理论组成部分是最近提出的均值漂移探索策略，该策略将大规模群体的协作效率提升了数十倍。此外，随着群体规模的增大，效率提升更加显著。最后，本文讨论了均值漂移探索策略的三个重要应用，包括精确形状形成、区域覆盖形成和机动编队，以及它们在智能仓储、区域探索和货物运输中相应的工业场景。
英文摘要：
Swarms evolving from collective behaviors among multiple individuals are commonly seen in nature, which enables biological systems to exhibit more efficient and robust collaboration. Creating similar swarm intelligence in engineered robots poses challenges to the design of collaborative algorithms that can be programmed at large scales. The assignment-based method has played an eminent role for a very long time in solving collaboration problems of robot swarms. However, it faces fundamental limitations in terms of efficiency and robustness due to its unscalability to swarm variants. This article presents a tutorial review on recent advances in assignment-free collaboration of robot swarms, focusing on the problem of shape formation. A key theoretical component is the recently developed \emph{mean-shift exploration} strategy, which improves the collaboration efficiency of large-scale swarms by dozens of times. Further, the efficiency improvement is more significant as the swarm scale increases. Finally, this article discusses three important applications of the mean-shift exploration strategy, including precise shape formation, area coverage formation, and maneuvering formation, as well as their corresponding industrial scenarios in smart warehousing, area exploration, and cargo transportation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
