Author(s)

Hongzhe Fan ¹

Affiliation(s)

¹ Aeronautical Engineering Institute, Civil Aviation University of China, Tianjin, China

Corresponding Author

Hongzhe Fan

ABSTRACT

At present, there are more and more situations using drones to perform missions, and the working environment of drones is becoming more and more complex. Path planning has become the basic premise for the smooth completion of missions. Although the Wolf pack algorithm is fast and robust in trajectory planning, which has good results for solving problems with complex high dimensions and multiple peaks. In view of the premature convergence, the poor global optimization ability and the final result does not reach the optimal route of Wolf package algorithm (WPA), and the slow convergence of genetic algorithm, an improved trajectory planning method is proposed. First, the equivalent terrain simulation method is used to equivalent and analyze the terrain obstacles in the working environment with the mountains, and constructed the equivalent terrain map of the work of UAV. Planning known UAV orbit at the beginning and end positions, simulations in matlab found that the improved Wolf pack algorithm can find the shortest path with shorter time. Eventually, our simulation results are better than using the Wolf Pack Algorithm or the Genetic Algorithm.

KEYWORDS

The threat-equivalent peak topography, UAV Path Planning, Wolf Pack Algorithm

CITE THIS PAPER

Hongzhe Fan, UAV Path Planning Method Based on Improved Wolf Pack Algorithm. Journal of Electronics and Information Science (2023) Vol. 8: 31-37. DOI: http://dx.doi.org/10.23977/10.23977/jeis.2023.080505.

REFERENCES

[1] Sadaf J, Ali H, Rizwan A, et al. UAV trajectory planning for disaster scenarios[J]. Vehicular Communications, 2023, 39.
[2] Stefan I, Bojan C, Luka G, et al. Multi-UAV trajectory planning for 3D visual inspection of complex structures [J]. Automation in Construction, 2023, 147.
[3] Chen Yongbo, Mei Yuesong, Yu Jianqiao, et al. Three-dimensional unmanned aerial vehicle path planning using modified wolf pack search algorithm[J]. Neurocomputing, 2017, 266: 445-457. 
[4] Chen Xu, Zhang Yi, Li Kui et al, Path Planning of Mobile Robot Based on Improved Wolf Swarm Algorithms, 2019 IEEE 8th Joint International Information Technology and Artificial Intelligence Conference (ITAIC), 2019, pp. 359-364. 
[5] Zhou Jing. Workshop robot navigation path planning of chaotic Wolf pack round-up algorithm [J]. Mechanical Design and Manufacture, 2020 (01): 251-255.

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