Research on Intelligent Path Planning for Unmanned Aerial Vehicles in Complex Environments

Peiye Sun

doi:10.54097/gmg9fd89

Authors

Peiye Sun

DOI:

https://doi.org/10.54097/gmg9fd89

Keywords:

Obstacle Avoidance for Unmanned Aerial Vehicles, Path Planning, Algorithm Fusion

Abstract

Aiming at the problems of insufficient real-time performance and safety in path planning for unmanned aerial vehicles (UAVs) in dynamic and complex environments, this study proposes a collaborative path planning method that integrates binocular vision environmental perception, ant colony algorithm for global optimization, and artificial potential field method for local dynamic obstacle avoidance, with the goal of improving the accuracy and robustness of autonomous navigation. This method precisely captures dynamic environmental information through binocular vision, optimizes the global path search using the ant colony algorithm, and combines the artificial potential field method to achieve real-time local obstacle avoidance. It not only enables rapid identification and response to complex environments but also significantly enhances the accuracy and robustness of UAV autonomous navigation.

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References

[1] Kumar, J., Himanshu, K., Kandath, H., Agrawal, P. (2023). Vision based UAV Navigation through Narrow Passages. arXiv:2303.15803v1 [cs.RO]. https://arxiv.org/ abs/2303. 1580 3v1.

[2] Zhang, X., Chen, Y. R., Zhang, X. W., Wang, H. N., Meng, Y. (2023). Research on UAV Power Line Detection and Obstacle Avoidance Based on Binocular Vision Algorithm. Manufacturing Automation, 45(9), 22–25.

[3] Hou, Y. H., Liu, Y., Lü, H. L., Wu, Q., Zhao, J., Chen, Y. F. (2019). An Autonomous Navigation System for UAV Based on Binocular Vision. Journal of Tianjin University (Natural Science and Engineering Edition), 52(12), 1262–1269.

[4] Yu, S. J., Feng, J. H., Qin, J. M., Chen, L. (2024). Three-Dimensional Path Planning for Unmanned Aircraft Based on Improved Artificial Potential Field. Yunnan Electric Power Technology, 52(3), 64–68.

[5] Yan, J., Ye, T. (2024). UAV Path Planning Optimized by Ant Colony Algorithm. Information and Computer, (10), 172–174.

[6] Song, H., Jia, M. H., Lian, Y. H., Fan, Y. J., Liang, K. S. (2022). UAV Path Planning Based on an Improved Ant Colony Algorithm. Journal of Electronic Research and Application, 6(2), 10–25.

[7] Bounini, F., Gingras, D., Pollart, H., Gruyer, D. (2017). Modified Artificial Potential Field Method for Online Path Planning Applications. In Proceedings of the IEEE Intelligent Vehicle Symposium, Redondo Beach, USA, 180–185. https:// doi. org/10.1109/IVS.2017.7995717.

[8] Irzoqe, F. N., Nasser, A. R., Raheem, F. A. (2024). A New Approach for Path Planning Algorithm Utilizing Modified Deep Q-Network Combined with Artificial Potential Field. International Journal of Intelligent Engineering & Systems, 17(6), 962–974. https://doi.org/10.22266/ijies2024.1231.72.

[9] Xiang, J., Xu, L., Liu, H. R., He, K. S., Fu, Y. H., Chen, H. (2024). UAV Path Planning in Complex Urban Environments. Information Technology and Informatization, (5), 195–198.

[10] Zhu, Y. F., Qiu, X. D., Li, S. (2025). Path Planning for Quadrotor UAV Using Improved Sparrow Search Algorithm. Sensors and Microsystems, 44(1), 141–145.