Clustering Algorithms in Swarm-Based Robotic Systems

Abstract

This article explores the application of clustering algorithms in swarm robotics, a field inspired by biological collectives and characterized by decentralized, self-organizing robotic behavior. Clustering, as a method of unsupervised learning, provides a foundation for grouping robots by behavior, location, or task allocation without central control. The article presents an overview of major clustering methods—such as K-Means, DBSCAN, OPTICS, and fuzzy clustering—and examines their adaptability for distributed, communication-constrained robotic systems. Practical applications in foraging, exploration, and task allocation are analyzed, along with associated challenges such as sensor noise, communication limitations, and dynamic environments. Performance metrics used to evaluate clustering effectiveness are also discussed. The study concludes that clustering algorithms significantly enhance coordination and task efficiency in swarm robotic systems and highlights the need for continued research to address real-world deployment issues and enable scalable, fault-tolerant solutions.