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RBFNN-Based Distributed Coverage Control on an Unknown Region

Author

Listed:
  • Ankang Zhang

    (College of Automation & Artificial Intelligence, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
    Jiangsu Engineering Center for IOT Intelligent Robots, Nanjing 210023, China)

  • Xiaoling Wang

    (College of Automation & Artificial Intelligence, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
    Jiangsu Engineering Center for IOT Intelligent Robots, Nanjing 210023, China)

Abstract

In this paper, we investigate the problem of achieving distributed coverage control of a mobile sensor network on an unknown region using local measurements. To accomplish this objective, each sensor is equipped with two-layer dynamics. The upper layer dynamic employs a completely distributed observer algorithm on the target region for state estimation of the density function. The lower layer dynamic utilizes a radial basis function neural network-based motion algorithm, which involves only the estimated state obtained by the upper layer dynamics, to guide the sensors towards an optimal coverage configuration. We demonstrate that with only the joint detectability of the partial outputs measurement, it is possible to achieve distributed coverage control in the unknown region without requiring additional information about the density function, communication topology associated with the sensors, or coupling gains. Finally, two examples are used to validate the theoretical findings.

Suggested Citation

  • Ankang Zhang & Xiaoling Wang, 2023. "RBFNN-Based Distributed Coverage Control on an Unknown Region," Mathematics, MDPI, vol. 12(1), pages 1-18, December.
    • Handle: RePEc:gam:jmathe:v:12:y:2023:i:1:p:111-:d:1309548
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    References listed on IDEAS

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    1. Jingyi Wang & Danqi Liu & Jianwen Feng & Yi Zhao, 2023. "Distributed Optimization Control for Heterogeneous Multiagent Systems under Directed Topologies," Mathematics, MDPI, vol. 11(6), pages 1-16, March.
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