无需分配的机器人集群协作新方法，效率提升数十倍。
Mean-Shift Theory and Its Applications in Swarm Robotics: A New Way to Enhance the Efficiency of Multi-Robot Collaboration
- 采用无任务分配的均值漂移探索策略，实现大规模集群协同。
- 在大规模集群中协作效率提升数十倍，规模越大效果越显著。
- 适用于智能仓储、区域勘探和货物运输等实际场景。
自然界中的群体行为催生了高效且鲁棒的协同机制，为工程化机器人集群提供了灵感。传统基于分配的协作方法在大规模应用中面临可扩展性瓶颈。本文综述了无分配协作的最新进展，聚焦于形状形成问题。核心理论是近期提出的均值漂移探索策略，该策略使大规模集群的协作效率提升数十倍，且随集群规模增大，增益更为显著。文章进一步讨论了该策略在精确形状构建、区域覆盖与机动编队中的三类应用，对应智能仓储、区域勘探与货物运输等工业场景。
原文摘要 · Abstract (English)
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.
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