Improved PID Search Algorithm for UAV Path Planning in Mountainous Environments

Authors

Yi Peng, School of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China; Yunnan Provincial Key Laboratory of Computer Science, Kunming University of Science and Technology, Kunming 650500, China
Yunkui Lei, School of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China; Yunnan Provincial Key Laboratory of Computer Science, Kunming University of Science and Technology, Kunming 650500, China
Qingqing Yang, School of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China; Yunnan Provincial Key Laboratory of Computer Science, Kunming University of Science and Technology, Kunming 650500, China
Hui Li, School of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China; Yunnan Provincial Key Laboratory of Computer Science, Kunming University of Science and Technology, Kunming 650500, China
Jianming Wang, School of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China; Yunnan Provincial Key Laboratory of Computer Science, Kunming University of Science and Technology, Kunming 650500, China

Abstract

Abstract: To address the challenges of UAV path planning in mountainous environments, including high computational complexity and suboptimal optimization performance, and the disadvantages of the PIDbased search algorithm, such as low optimization accuracy and slow convergence rate, this paper proposed an improved PID search algorithm (IPSA). The method introduced a good point set to ensure a more uniform population distribution, thereby enhancing population diversity and global search capability. The Q-learning algorithm was employed to adapt PID parameter adjustments, incorporating an exploration rate factor to further improve the algorithm's exploration and computational capabilities. A lens imaging opposition-based learning mechanism was also integrated to help the algorithm effectively avoid local optima and accelerate the convergence rate. Experimental results have demonstrated that compared with the PSA algorithm, the convergence accuracy of the IPSA algorithm increases by 3.5% in sparse environments and by 3.5% in complex environments, while the stability increases by 33.1% and 53.7% respectively, thereby significantly boosting UAV path planning capability in mountainous environments.

Recommended Citation

Peng, Yi; Lei, Yunkui; Yang, Qingqing; Li, Hui; and Wang, Jianming (2025) "Improved PID Search Algorithm for UAV Path Planning in Mountainous Environments," Journal of System Simulation: Vol. 37: Iss. 12, Article 9.
DOI: 10.16182/j.issn1004731x.joss.24-0721
Available at: https://dc-china-simulation.researchcommons.org/journal/vol37/iss12/9

First Page

3075

Last Page

3086

CLC

TP391.9

Recommended Citation

Peng Yi, Lei Yunkui, Yang Qingqing, et al. Improved PID Search Algorithm for UAV Path Planning in Mountainous Environments[J]. Journal of System Simulation, 2025, 37(12): 3075-3086.

Corresponding Author

Yang Qingqing

DOI

10.16182/j.issn1004731x.joss.24-0721