UAV Path Planning Based on an Improved Ant Colony Algorithm


Path planning
Ant colony algorithm




Reviews and experimental verification have found that existing solution methods can be used to solve UAV path planning problems, but each approximate solution has its own advantages and disadvantages. For example, ant colony algorithm easily falls into the local optimum in the process of realizing path planning. In order to prevent too low pheromones on the longer path and too high pheromones in the shorter path, the upper and lower limits of pheromones as well as their volatile factors are set to avoid falling into the local optimum. Secondly, multi-heuristic factors are introduced, and the overall length of the path serves as an adaptive heuristic function factor that determines the probability of state transition, which affects the probability of ants choosing the corresponding path. The experimental results show that the path length planned by the improved algorithm is 93.6% of the original algorithm, and the optimal path length variance is only 14.22% of the original algorithm. The improved ant colony algorithm shortens the optimal path length and solves the UAV path planning problem in terms of local optima. At the same time, multiple enlightening factors are introduced to increase the suitability of UAV for complex environments and improve the performance of UAV.


Tang L, Hao P, Zhang X, 2019, An UAV Path Planning Method in Mountainous Area Based on an Improved Ant Colony Algorithm. Journal of Transportation Systems Engineering and Information Technology, 19(1): 158-164.

Chen Y, Luo G, Mei Y, et al., 2016, UAV Path Planning Using Artificial Potential Field Method Updated by Optimal Control Theory. International Journal of Systems Science, 47(6): 1407-1420.

He Y, 2018, UAV Route Planning Based on Improved Dynamic Weighted A* Algorithm. Journal of Hebei University of Science and Technology, 39(4): 349-355.

Zhen R, Zhang C, Jiao Y, et al., 2019, Research on UAV Route Planning Based on Adaptive Polymorphic Ant Colony Algorithm. Journal of Hebei University of Science and Technology, 40(6): 526-532.

Wang Q, Ma L, Deng H, 2013, Adaptive Path Planning of the UAV Based on Genetic Algorithm. Computer Systems & Applications, 22(1): 200-203.

Xiong H, Yu B, He C, 2020, UAV Path Planning Method Based on Improved PSO. Computer Measurement & Control, 28(2): 144-147.

Wang GG, Chu HCE, Mirjalili S, 2016, Three-Dimensional Path Planning for UCAV Using an Improved Bat Algorithm. Aerospace Science and Technology, 49: 231-238.

Miao H, Tian Y, 2013, Dynamic Robot Path Planning Using an Enhanced Simulated Annealing Approach. Applied Mathematics and Computation, 222: 420-437.

Wu X, Xu L, Zhen R, et al., 2019, Dynamic Step BHRRT UAV Path Planning Algorithm. Journal of Hebei University of Science and Technology, 40(5): 414-422.

Zhao J, 2020, Unmanned Air Vehicle Route Planning Strategy Based on D* Algorithm Extension Guided by Inspiration Point. Machinery Design & Manufacture, 2020(2): 153-157.

Xu J, Liu L, 2019, 3D Trajectory Planning for UAV Based on Improved Artificial Fish Swarm Algorithm. Computer Engineering and Design, 40(2): 540-544.

Cheng Z, Li D, Gao Y, 2019, UAV Three-Dimensional Path Planning Based on the Grasshopper Algorithm. Flight Dynamics, 37(2): 46-50.

Yu S, Wu H, Ma J, 2018, Path Planning for Unmanned Air Vehicle Based on Chaotic Glowworm Swarm Optimization. Machinery Design & Manufacture, 2018(11): 113-116.

Lu T, Liu L, He Y, et al., 2020, Multi-UAV Path Planning Algorithm and Key Technology. Tactical Missile Technology, 2020(1): 85-90.

Xiao Z, Wang P, Leng S, et al., 2016, Research on the Double Precision Mission Planning in Multi-UCAV System. Tactical Missile Technology, 2016(3): 53-57.

Zhu L, Shen C, Shen B, et al., 2019, A Survey of Intelligent Optimization Algorithms for UAV Route Planning Group. Digital Technology and Application, 37(8): 126.

Wang H, Hu X, 2020, Research on UAV Trajectory Planning Based on Ant Colony Algorithm. Science and Technology Information, 18(10): 29-30.

Liu R, Yang F, Zhang H, 2018, Path Planning for UAV Based on Improved Chaotic Ant Colony Algorithm (CACA). Command Information System and Technology, 9(6): 41-48.

Zhang S, Pu J, Si Y, et al., 2020, Survey on Application of Ant Colony Algorithm in Path Planning of Mobile Robot. Computer Engineering and Applications, 56(8): 10-19.

Wang Q, Liu M, Ren W, et al., 2019, Overview of Common Algorithms for UAV Path Planning. Journal of Jin University (Information Science Edition), 37(1): 58-67.

Huang L, Qu H, Ji P, et al., 2016, A Novel Coordinated Path Planning Method Using K-Degree Smoothing for Multi-UAVs. Applied Soft Computing, 48: 182-192.

Tao J, Wang Y, Yang H, et al., 2016, Proceedings of the 28th Chinese Control and Decision Conference (2016 CCDC), May 26-28, 2016: Three-Dimensional Path Planning of Unmanned Aerial Vehicle Under Complicated Environment. IEEE Industrial Electronics (IE) Chapter, Yinchuan, China.

Sun G, Su Y, Gu Y, et al., 2019, Path Planning for Unmanned Surface Vehicle Based on Improved Ant Colony Algorithm. Control and Decision, 36(04): 0839. DOI:

Huang X, Xiang Y, 2019, Simulation Research on UAV Route Planning Based on Improved Ant Colony Algorithm. Urban Geotechnical Investigation & Surveying, 2019(1): 83-87.

Li L, Li H, Shan N, 2019, Path Planning Based on Improved Ant Colony Algorithm with Multiple Inspired Factor. Computer Engineering and Applications, 55(5): 219-225.

Chen J, Huang W, Wang X, et al., 2020, Research on Path Planning Based on an Improved Ant Colony Algorithm for Mobile Robot. Chinese High Technology Letters, 30(3): 291-297.

Akka K, Khaber F, 2018, Mobile Robot Path Planning Using an Improved Ant Colony Optimization. International Journal of Advanced Robotic Systems, 15(3): 1-7.