Review of Autonomous Flight Control Technology for Unmanned Aerial Vehicles in Complex Environments

Authors

  • Aolin Hou

DOI:

https://doi.org/10.54691/63cn9j94

Keywords:

Unmanned Aerial Vehicles (UAVs); Autonomous Flight Control; Complex Environments; Environmental Perception; Robust Control.

Abstract

With the rapid advancement of Unmanned Aerial Vehicles (UAVs) technology, its application value and penetration have been continuously enhanced in various fields, especially in surveying and mapping exploration, emergency rescue, logistics and transportation, and military fields. However, in some complex environments such as urban building clusters, indoor spaces, mountainous terrain, and areas with dynamic obstacles, achieving safe and efficient autonomous flight of UAVs still faces numerous difficulties and challenges. This paper systematically reviews the autonomous flight control technology of UAVs in these complex environments, elaborates on the core technical framework of this field, and introduces the latest research achievements and future development trends at home and abroad. It focuses on four key modules: environmental perception, path planning, obstacle avoidance, and robust control systems. Research shows that multi-sensor fusion technology for perception, intelligent path planning based on deep reinforcement learning, multi-UAV cooperative obstacle avoidance technology, and robust control technology driven by reinforcement learning have become the main research trends in this field. By comprehensively sorting out relevant research results at home and abroad, this paper constructs the context of technological development, hoping to provide certain reference for future research in the field of UAV autonomous flight control.

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References

[1] Sabuj, S. R., Cho, Y., Elsharief, M., & Jo, H. S. (2025). Trajectory design of UAV-aided energy-harvesting relay networks in the terahertz band. Computer Communications, 230, 108007.

[2] Chen, X., Zhang, Y., Qu, C., Fan, C., Wang, T., & Liu, S. (2023). Design and implementation of an autonomous control system for simulated fixed-wing unmanned aerial vehicles. In Proceedings of the 2023 3rd International Conference on Robotics, Automation and Intelligent Control (ICRAIC) (pp. 240–245). IEEE.

[3] He, W., Qi, X., & Liu, L. (2021). A novel hybrid particle swarm optimization for multi-UAV cooperative path planning. Applied Intelligence, 51(10), 7350–7364.

[4] Shafiq, M., Ali, Z. A., Israr, A., Alkhammash, E. H., & Hadjouni, M. (2022). A multi-colony social learning approach for the self-organization of a swarm of UAVs. Drones, 6(5), 104.

[5] Jiang, Y., Xu, X. X., Zheng, M. Y., & Zhan, Z. H. (2024). Evolutionary computation for unmanned aerial vehicle path planning: A survey. Artificial Intelligence Review, 57(10).

[6] Machmudah, A., Shanmugavel, M., Parman, S., Manan, T. S. A., Dutykh, D., & Beddu, S. (2022). Flight trajectories optimization of fixed-wing UAV by bank-turn mechanism. Drones, 6(3), 69.

[7] Muzahid, A. J. M., Kamarulzaman, S. F., Rahman, M. A., Murad, S. A., Kamal, M. A. S., & Alenezi, A. H. (2023). Multiple vehicle cooperation and collision avoidance in automated vehicles: Survey and an AI-enabled conceptual framework. Scientific Reports, 13(1).

[8] Xie, R., Meng, Z., Wang, L., Li, H., Wang, K., & Wu, Z. (2021). Unmanned aerial vehicle path planning algorithm based on deep reinforcement learning in large-scale and dynamic environments. IEEE Access, 9, 24884–24900.

[9] Wang, P., Su, H., Zhang, Z., & Huo, M. (2025). Autonomous planning, navigation and control for lightweight unmanned aerial vehicles in cluttered environments. Guidance, Navigation and Control, 5(2), 291–296.

[10] Zhang, R., Li, S., Ding, Y., Qin, X., & Xia, Q. (2022). UAV path planning algorithm based on improved Harris Hawks optimization. Sensors, 22(14), 5232.

[11] Jhang, J. W., & Juang, J. G. (2023). Application of path planning and obstacle avoidance for riverbank inspection. Sensors, 23(22), 9253.

[12] Chen, B., Cui, Y., Zhong, P., Yang, W., Liang, Y., & Wang, J. (2023). STExplorer: A hierarchical autonomous exploration strategy with spatio-temporal awareness for aerial robots. ACM Transactions on Intelligent Systems and Technology, 14(6), 1–24.

[13] Bashir, N., Boudjit, S., Dauphin, G., & Zeadally, S. (2023). An obstacle avoidance approach for UAV path planning. Simulation Modelling Practice and Theory, 129, 102815.

[14] Merei, A., Mcheick, H., Ghaddar, A., & Rebaine, D. (2025). A survey on obstacle detection and avoidance methods for UAVs. Drones, 9(3), 203.

[15] Zhou, Y., Kong, X., Lin, K. P., & Liu, L. (2024). Novel task decomposed multi-agent twin delayed deep deterministic policy gradient algorithm for multi-UAV autonomous path planning. Knowledge-Based Systems, 287, 111462.

[16] Yang, L., Li, Y., Tian, J., & Wang, D. (2025). Fully autonomous anti-interference flight control of vertical takeoff and landing unmanned aerial vehicles in satellite positioning rejection environment. Measurement Science and Technology, 36(3), 036214.

[17] Zhu, Y., Zhang, T., Wu, A., & Shi, G. (2025). PISCFF-LNet: A method for autonomous flight of UAVs based on lightweight road extraction. Drones, 9(3), 226.

[18] Xiong, W., Wang, Z., Niu, M., & Liang, Q. (2024). Low-altitude UAV autonomous obstacle avoidance using LiDAR sensor fusion. In Proceedings of the 2024 10th International Conference on Mechanical and Electronic Engineering (ICMEE) (pp. 201–206). IEEE.

[19] Yin, Y., Wang, Z., Zheng, L., Su, Q., & Guo, Y. (2024). Autonomous UAV navigation with adaptive control based on deep reinforcement learning. Electronics, 13(13), 2432.

[20] Zhao, Y., Yan, L., Dai, J., Hu, X., Wei, P., & Xie, H. (2023). Robust planning system for fast autonomous flight in complex unknown environment using sparse directed frontier points. Drones, 7(3), 219.

[21] Tang, Q., & Niu, Y. (2024). Research on autonomous obstacle avoidance for indoor UAVs based on vision and laser. In Proceedings of the 2024 International Conference on Interactive Intelligent Systems Technology (IIST) (pp. 73–80). IEEE.

[22] Zhao, L., Yan, L., Hu, X., Yuan, J., & Liu, Z. (2021). Efficient and high path quality autonomous exploration and trajectory planning of UAV in an unknown environment. ISPRS International Journal of Geo-Information, 10(10), 631.

[23] Du, H., Wang, Z., & Zhang, X. (2023). EF-TTOA: Development of a UAV path planner and obstacle avoidance control framework for static and moving obstacles. Drones, 7(6), 359.

[24] Wang, J., Zhao, Z., Qu, J., & Chen, X. (2024). APPA-3D: An autonomous 3D path planning algorithm for UAVs in unknown complex environments. Scientific Reports, 14(1).

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Published

2026-05-14

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Section

Articles

How to Cite

Hou, Aolin. 2026. “Review of Autonomous Flight Control Technology for Unmanned Aerial Vehicles in Complex Environments”. Scientific Journal of Intelligent Systems Research 8 (4): 43-50. https://doi.org/10.54691/63cn9j94.