Mean-Shift Theory and Its Applications in Swarm Robotics: A New Way to Enhance the Efficiency of Multi-Robot Collaboration

TL;DR

This paper reviews recent advances in assignment-free swarm robot collaboration, highlighting a mean-shift exploration strategy that significantly enhances efficiency and scalability in tasks like shape formation and area coverage.

Contribution

It introduces the mean-shift exploration strategy as a scalable, assignment-free method to improve large-scale swarm collaboration efficiency, surpassing traditional assignment-based approaches.

Findings

Mean-shift exploration improves collaboration efficiency by dozens of times.

Efficiency gains increase with swarm size.

Applications include shape formation, area coverage, and maneuvering in industrial scenarios.

Abstract

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.