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A Multi-UAV cooperative mission planning method based on SA-WOA algorithm for three-dimensional space atmospheric environment detection

Published online by Cambridge University Press:  22 May 2024

Binggang Yu Open the ORCID record for Binggang Yu [Opens in a new window] ,
Shurui Fan ,
Weijia Cui ,
Kewen Xia  and
Li Wang
Binggang Yu
Affiliation:
Tianjin Key Laboratory of Electronic Materials Devices, School of Electronic and Information Engineering, Hebei University of Technology, Tianjin, China
Shurui Fan*
Affiliation:
Tianjin Key Laboratory of Electronic Materials Devices, School of Electronic and Information Engineering, Hebei University of Technology, Tianjin, China
Weijia Cui
Affiliation:
The 54th Research Institute of CETC, Shijiazhuang, China
Kewen Xia
Affiliation:
Tianjin Key Laboratory of Electronic Materials Devices, School of Electronic and Information Engineering, Hebei University of Technology, Tianjin, China
Li Wang
Affiliation:
Tianjin Key Laboratory of Electronic Materials Devices, School of Electronic and Information Engineering, Hebei University of Technology, Tianjin, China
*
Corresponding author:Shurui Fan; Email: fansr@hebut.edu.cn
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Abstract

In the application of rotorcraft atmospheric environment detection, to reflect the distribution of atmospheric pollutants more realistically and completely, the sampling points must be spread throughout the entire three-dimensional space, and the cooperation of multiple unmanned aerial vehicles (multi-UAVs) can ensure real-time performance and increase operational efficiency. In view of the problem of coordinated detection by multi-UAVs, the region division and global coverage path planning of the stereo space to be detected are studied. A whale optimization algorithm based on the simulated annealing-whale optimization algorithm (SA-WOA) is proposed, which introduces adaptive weights with the Levy flight mechanism, improves the metropolis criterion, and introduces an adaptive tempering mechanism in the SA stage. Path smoothing is subsequently performed with the help of nonuniform rational B-spline (NURBS) curves. The comparison of algorithms using the eil76 dataset shows that the path length planned by the SA-WOA algorithm in this paper is 10.15% shorter than that of the WOA algorithm, 13.25% shorter than the SA planning result, and only 0.95% difference from the optimal path length in the dataset. From the perspective of planning time, its speed is similar to WOA, with a relative speed increase of 27.15% compared to SA, proving that the algorithm proposed in this paper has good planning performance. A hardware system platform is designed and built, and environmental gas measurement experiments were conducted. The experimental results indicate that the multi-UAV collaborative environment detection task planning method proposed in this paper has certain practical value in the field of atmospheric environment detection.

Keywords

atmospheric environmental detectionmulti-UAV collaboration mission planningsimulated annealing algorithmwhale optimization algorithmmetaheuristic algorithms
Type
Research Article
Information
Robotica , First View , pp. 1 - 38
Copyright
© The Author(s), 2024. Published by Cambridge University Press

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