Dynamic Clustering of Multi-Mobile Robot System using Gaussian Mixture Model
DOI:
https://doi.org/10.18196/jrc.v6i5.27184Keywords:
Dynamic Cluster, Gaussian Mixture Model, Expectation-Maximization Algorithm, Path Planning, Workload Balancing, Multi-Mobile RobotsAbstract
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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