Dynamic Clustering of Multi-Mobile Robot System using Gaussian Mixture Model - Xuan, Hung Truong; Manh, Thang Pham; Quang, Nha Nguyen; Hong, Hanh Nguyen Thi (Universitas Muhammadiyah Yogyakarta, 2025)
Managing large fleets of mobile robots poses significant challenges to system coordination and workload. An effective grouping strategy is crucial for enhancing operational performance and scalability. This paper introduces a two-stage dynamic clustering method (DCM), a novel framework for organizing robots into manageable groups. The methodology utilizes a Gaussian Mixture Model and the Expectation-Maximization algorithm to cluster robots based on their path intersection points. A unique