<![CDATA[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 "cost" parameter, formulated a least squares objective function, is proposed to guide the selection of near-optimal, workload-balanced configurations. The results from extensive simulations demonstrated the framework's effectiveness. On a single dataset, DCM exhibited exceptional reliability, maintaining a stable objective function value even as the number of robots per cluster fluctuated across runs. A sensitivity analysis over multiple unique datasets confirmed the model's adaptive strength, showing its ability to re-configure clusters. This adaptability was highlighted by the mean objective function value varying across different scenarios. Further analysis involving reduced robot populations and obstacle-filled environments validated DCM's generalizability and environment-independent nature. The robot distribution mechanism was consistently equitable and balanced. Statistical validation, including bootstrapping resamples, confirmed the stability and reliability of the performance estimates. The method also steadily maintained a high level of performance by adapting to internal variations. Moreover, every robot was successfully assigned to all clusters across all trials. The research concludes that DCM is a robust, adaptive, and environment-independent framework. It successfully balances performance stability with the flexibility to respond to new operational conditions, proving it is an effective solution for multi-robot coordination.]]>
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