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Multi-robot area coverage and source localization method based on variational sparse Gaussian process

Published online by Cambridge University Press:  21 May 2025

Kai Cao Open the ORCID record for Kai Cao [Opens in a new window] ,
Yunbo Wei Open the ORCID record for Yunbo Wei [Opens in a new window] ,
Yangquan Chen ,
Song Gao Open the ORCID record for Song Gao [Opens in a new window] ,
Kun Yan  and
Shibo Yang
Kai Cao
Affiliation:
School of Electronic Information Engineering, Xi’an Technological University, Xi’an, China School of Engineering (MESA-Lab), University of California, Merced, CA, USA
Yunbo Wei
Affiliation:
School of Electronic Information Engineering, Xi’an Technological University, Xi’an, China
Yangquan Chen
Affiliation:
School of Electronic Information Engineering, Xi’an Technological University, Xi’an, China School of Engineering (MESA-Lab), University of California, Merced, CA, USA
Song Gao*
Affiliation:
School of Electronic Information Engineering, Xi’an Technological University, Xi’an, China
Kun Yan
Affiliation:
School of Electronic Information Engineering, Xi’an Technological University, Xi’an, China
Shibo Yang
Affiliation:
School of Electronic Information Engineering, Xi’an Technological University, Xi’an, China
*
Corresponding author: Song Gao; Email: gaos@xatu.edu.cn
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Abstract

This paper introduces a distributed online learning coverage control algorithm based on sparse Gaussian process regression for addressing the problem of multi-robot area coverage and source localization in unknown environments. Considering the limitations of traditional Gaussian process regression in handling large datasets, this study employs multiple robots to explore the task area to gather environmental information and approximate the posterior distribution of the model using variational free energy methods, which serves as the input for the centroid Voronoi tessellation algorithm. Additionally, taking into consideration the localization errors, and the impact of obstacles, buffer factors and centroid Voronoi tessellation algorithms with separating hyperplanes are introduced for dynamic robot task area planning, ultimately achieving autonomous online decision-making and optimal coverage. Simulation results demonstrate that the proposed algorithm ensures the safety of multi-robot formations, exhibits higher iteration speed, and improves source localization accuracy, highlighting the effectiveness of model enhancements.

Keywords

multi-robotcentroid Voronoi tessellationvariational sparse Gaussian processbuffering factorsource localization
Type
Research Article
Information
Robotica , First View , pp. 1 - 21
Copyright
© The Author(s), 2025. Published by Cambridge University Press

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