Version 1
: Received: 22 September 2024 / Approved: 23 September 2024 / Online: 24 September 2024 (03:51:21 CEST)
How to cite:
Yang, Y.; Fu, J. Y.; Lu, Y. D.; Xiang, H. H. 3D UAV Trajectory Planning Based on Improved Whale Optimization Algorithm. Preprints2024, 2024091710. https://doi.org/10.20944/preprints202409.1710.v1
Yang, Y.; Fu, J. Y.; Lu, Y. D.; Xiang, H. H. 3D UAV Trajectory Planning Based on Improved Whale Optimization Algorithm. Preprints 2024, 2024091710. https://doi.org/10.20944/preprints202409.1710.v1
Yang, Y.; Fu, J. Y.; Lu, Y. D.; Xiang, H. H. 3D UAV Trajectory Planning Based on Improved Whale Optimization Algorithm. Preprints2024, 2024091710. https://doi.org/10.20944/preprints202409.1710.v1
APA Style
Yang, Y., Fu, J. Y., Lu, Y. D., & Xiang, H. H. (2024). 3D UAV Trajectory Planning Based on Improved Whale Optimization Algorithm. Preprints. https://doi.org/10.20944/preprints202409.1710.v1
Chicago/Turabian Style
Yang, Y., Yang Dong Lu and Hui Hong Xiang. 2024 "3D UAV Trajectory Planning Based on Improved Whale Optimization Algorithm" Preprints. https://doi.org/10.20944/preprints202409.1710.v1
Abstract
Trajectory planning determines whether a UAV can successfully complete its mission, and the reasonable planning of UAV trajectory in 3D environment is a complex global optimization problem, which needs to take into account many constraints such as urban environment, mountain terrain, obstacles, no-fly zones, flight boundaries, flight distances, and trajectory change rates. In view of the shortcomings of the whale optimization algorithm in 3D trajectory planning, such as slow convergence speed, low accuracy and easy to fall into the local optimum, this paper increases the diversity of the initial population through the introduction of the reverse learning mechanism, and optimizes the coordination of the global and local search ability by integrating the nonlinear convergence factor and the random number generating mechanism, so as to realize the improved whale optimization algorithm that can cope with the higher degree of freedom and the more complex constraints. The simulation results show that the optimization algorithm has improved the convergence accuracy by 22.1% and reduced the standard deviation by 74.1%, which can effectively deal with the 3D UAV path planning problem.
Keywords
unmanned aerial vehicle; 3D path planning; whale optimization algorithm; inverse learning; non-linear convergence; random number generation
Subject
Engineering, Civil Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
