RBFNN-Based Distributed Coverage Control on an Unknown Region

Ankang Zhang and Xiaoling Wang ()
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Ankang Zhang: College of Automation & Artificial Intelligence, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
Xiaoling Wang: College of Automation & Artificial Intelligence, Nanjing University of Posts and Telecommunications, Nanjing 210023, China

Mathematics, 2023, vol. 12, issue 1, 1-18

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.

Keywords: coverage control; distributed observer; density function; radial basis function neural network (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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