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Neural network-based velocity-controllable UAV flocking

Published online by Cambridge University Press:  23 June 2022

T. He Open the ORCID record for T. He [Opens in a new window]  and
L. Wang Open the ORCID record for L. Wang [Opens in a new window]
T. He
Affiliation:
Research Institute of Electronic Science and Technology, University of Electronic Science and Technology of China, Chengdu China
L. Wang*
Affiliation:
Research Institute of Electronic Science and Technology, University of Electronic Science and Technology of China, Chengdu China
*
*Corresponding author. Email: wang_lei@uestc.edu.cn
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Abstract

The unmanned aerial vehicle (UAV) flocking among obstacles was transferred to a velocity-controllable UAV flocking problem, which means that multi-UAV gradually form and maintain the $\alpha$ -lattice geometry as they track the desired flocking velocity, and can be applied to tasks such as obstacle avoidance and velocity tracking. Velocity-controllable UAV flocking problem is a multi-objective flocking controller parameters optimisation problem, for which we design flocking velocity and geometry objective function, and solve them using a multi-objective particle swarm optimisation algorithm (MOPSO). On this basis, to address the problem that MOPSO has random results and long computation time, we propose to use a neural network (NN) to approximate the mathematical relationship between the UAV flocking state and the flocking controller parameters. We simulate the flight process of 5 and 49 UAVs performing obstacle avoidance and velocity tracking tasks, respectively. The results show that the proposed UAV flocking controller has better convergence performance, obtains reproducible results, reduces computation time, and can be used for large-scale UAV flocking control.

Keywords

Unmanned aerial vehicle (UAV)FlockingMulti-objective particle swarm optimisation (MOPSO)Neural network (NN)Obstacle avoidance
Type
Research Article
Information
The Aeronautical Journal , Volume 127 , Issue 1308 , February 2023 , pp. 289 - 304
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Copyright
© The Author(s), 2022. Published by Cambridge University Press on behalf of Royal Aeronautical Society

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Footnotes

The two authors, Tao He and Lei Wang, are co-first authors.

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