Research on Unmanned Aerial Vehicle Path Planning


  • Lingzhi Jiang
  • Qiwu Wu
  • Weicong Tan
  • Tao Tong
  • Weiyi Zhang



Unmanned Aerial Vehicle, Path Planning, Intelligent Optimization Algorithms


This paper reviews and analyses the research progress in the field of UAV path planning. Firstly, the importance of UAV path planning and the current research work related to UAV path planning are introduced. Then how UAV path planning is modelled is analysed and key issues to be considered are given. Finally, classical search algorithms, evolutionary algorithms, heuristic search-based algorithms and deep learning methods are analysed in UAV path planning. For each method, its principle, characteristics, advantages and disadvantages, and applicable scenarios are analysed. The aim of this paper is to provide a comprehensive overview for researchers and scholars in the field of UAV path planning in order to promote the development and application of related technologies.


Download data is not yet available.


Guo Y, Liu X, Zhang W, et al. 3D path planning method for UAV based on improved artificial potential field[J]. Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University, 2020, 38(5): 977-986.

Cao L, Wang L, Liu Y, et al. 3D trajectory planning based on the Rapidly-exploring Random Tree–Connect and artificial potential fields method for unmanned aerial vehicles[J]. International Journal of Advanced Robotic Systems, 2022, 19(5): 17298806221118867.

Zhao Y, Zheng Z, Liu Y. Survey on computational-intelligence-based UAV path planning[J]. Knowledge-Based Systems, 2018, 158: 54-64.

Souto A, Alfaia R, Cardoso E, et al. UAV path planning optimization strategy: Considerations of urban morphology, microclimate, and energy efficiency using Q-learning algorithm [J]. Drones, 2023, 7(2): 123.

Deng W, Chen R, He B, et al. A novel two-stage hybrid swarm intelligence optimization algorithm and application[J]. Soft Computing, 2012, 16: 1707-1722.

Yang F, Wang P, Zhang Y, et al. Survey of swarm intelligence optimization algorithms[C]//2017 IEEE international conference on unmanned systems (ICUS). IEEE, 2017: 544-549.

Mavrovouniotis M, Li C, Yang S. A survey of swarm intelligence for dynamic optimization: Algorithms and applications[J]. Swarm and Evolutionary Computation, 2017, 33: 1-17.

Yinggao Yue, Li Cao, Haishao Chen, Yaodan Chen, and Zhonggen Su. 2023. Towards an Optimal KELM Using the PSO-BOA Optimization Strategy with Applications in Data Classification. Biomimetics, 8, no. 3: 306.

Cao Li, Wang Z, Wang Z, Wang X, Yinggao Yue*. An Energy-Saving and Efficient Deployment Strategy for Heterogeneous Wireless Sensor Networks Based on Improved Seagull Optimization Algorithm. Biomimetics. 2023; 8(2):231.

He Y, Wang M. An improved chaos sparrow search algorithm for UAV path planning[J]. Scientific reports, 2024, 14(1): 366.

Zhu H, Wang Y, Li X. UCAV path planning for avoiding obstacles using cooperative co-evolution spider monkey optimization [J]. Knowledge-Based Systems, 2022, 246: 108713.

Jiaqi S, Li T, Hongtao Z, et al. Adaptive multi-UAV path planning method based on improved gray wolf algorithm[J]. Computers and Electrical Engineering, 2022, 104: 108377.







How to Cite

Jiang , L., Wu , Q., Tan, W., Tong, T., & Zhang, W. (2024). Research on Unmanned Aerial Vehicle Path Planning. Frontiers in Computing and Intelligent Systems, 8(3), 22-24.