Three-Dimensional Path Planning of UAV Based on All Particles Driving Wild Horse Optimizer Algorithm

Authors

Gaoyang Li, College of Mechatronics Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210000, ChinaFollow
Xiangfeng Li, College of Mechatronics Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210000, ChinaFollow
Kang Zhao, College of Mechatronics Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210000, China
Yuchao Jin, College of Mechatronics Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210000, China
Zhidong Yi, College of Mechatronics Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210000, China
Dunwen Zuo, College of Mechatronics Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210000, China

Abstract

Abstract: In view of large calculation amounts and difficult convergence in the unmanned aerial vehicle (UAV) path planning, a path planning method based on all particles driving wild horse optimizier (APDWHO) was proposed. A three-dimensional environment model and path cost model were established, by which the path planning problem was transformed into a multi-dimensional function optimization problem. An adaptive neighborhood search strategy (ANSS) was adopted to improve the exploitation ability of the algorithm. The Gaussian random walk strategy was used to search the historical optimal position of the individual to improve the exploration ability of the algorithm. Since the ANSS is sensitive to the diversity of the initial population, the Tent chaotic map was used to initialize the population to improve the robustness and the global optimization ability of the algorithm. The performance of the improved algorithm was verified in 13 classic test functions and transplanted to the 3D path planning problem of UAVs. The test was conducted under the environment models of 30, 40, and 50 peaks. Compared with genetic algorithm (GA), particle swarm optimization (PSO), self-regulating and self-perception particle swarm optimization with mutation mechanism (SRM-PSO), and wild horse optimizer (WHO), APDWHO achieved the shortest average path and found the path that satisfies constraints and is collision-free in all tests. The simulation results show that the APDWHO has excellent global optimization ability and good robustness in complex environments.

Recommended Citation

Li, Gaoyang; Li, Xiangfeng; Zhao, Kang; Jin, Yuchao; Yi, Zhidong; and Zuo, Dunwen (2024) "Three-Dimensional Path Planning of UAV Based on All Particles Driving Wild Horse Optimizer Algorithm," Journal of System Simulation: Vol. 36: Iss. 3, Article 6.
DOI: 10.16182/j.issn1004731x.joss.22-1252
Available at: https://dc-china-simulation.researchcommons.org/journal/vol36/iss3/6

First Page

595

Last Page

607

CLC

V279; TP391.9

Recommended Citation

Li Gaoyang, Li Xiangfeng, Zhao Kang, et al. Three-Dimensional Path Planning of UAV Based on All Particles Driving Wild Horse Optimizer Algorithm[J]. Journal of System Simulation, 2024, 36(3): 595-607.

Corresponding Author

Li Xiangfeng

DOI

10.16182/j.issn1004731x.joss.22-1252