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.

Key words: wild horse optimizer, adaptive neighborhood search, Gaussian random walk, Tent chaotic mapping, path planning of UAV, all particles driving