|
|
|
| Perception-Driven-Controlled UAV Interception and Collision Technology |
| WANG Zhibo1， HU Weijun1， MA Xianlong1， QUAN Jiale2， ZHOU Haoyu3 |
| 1. School of Astronautics， Northwestern Polytechnical University， Xi’an 710072， Shaanxi， China；
2. School of Aeronautics and Astronautics， Xi’an Jiaotong University， Xi’an 710049， Shaanxi， China；
3. Zhiyuan Research Institute， Hangzhou 310012， Zhejiang， China |
|
|
|
|
Abstract Unmanned Aerial Vehicles (UAVs) possess inherent advantages including high maneuverability, compact size, ease of modification, low-altitude slow flight capability, and adaptability to complex environments. These features enable tasks to be completed efficiently and gather information effectively. However, the same advantages also introduce security risks from unauthorized UAVs operating in restricted areas, which are compounded by the significant interception challenges posed by their high speed, low altitude, and highly maneuverable characteristics. To address these challenges, this study focused on the critical technical problems of autonomous decision-making and trajectory control for high-speed, precise UAV interception. A method for intercepting target UAVs using an interceptor UAV was proposed, whose core approach was an end-to-end deep reinforcement learning (DRL) network framework. This framework utilized the proximal policy optimization (PPO) algorithm to train a neural network controller that directly maps perceptual information to forces and torques controlling the quadrotor UAV. To optimize interception performance, a novel reward function based on reward shaping techniques was designed. This function enabled the interceptor UAV to achieve faster, smoother, and more precise interception trajectories. Experimental results demonstrate that the proposed method allows high-speed interception and achieves superior interception accuracy. Throughout interactions within the environment, the interceptor UAV demonstrates robust adaptive adjustment and real-time decision-making capabilities. This study verifies the effectiveness of the end-to-end DRL-based UAV interception solution. The method efficiently and precisely accomplishes high-speed interception tasks, while its end-to-end nature significantly reduces reliance on precise UAV dynamic models.
|
|
Received: 03 December 2024
Published: 09 September 2025
|
|
|
|
|
|
| [1] |
GE Luqin, DING Shizhou, YAO Qiang, ZHANG Cheng, HUANG Yuchen. Research Review of Electromagnetic Interference Mechanism and Anti-Interference Technology for UAVs[J]. Air & Space Defense, 2025, 8(4): 51-55. |
| [2] |
ZHOU Wenjie, FU Yulong, GUO Xiangke, QI Yutao, ZHANG Haibin. Air Combat Decision-Making Method Based on Game Tree and Digital Parallel Simulation Battlefield[J]. Air & Space Defense, 2025, 8(3): 50-58. |
| [3] |
LI Xu, DONG Wei, DU Zehong. Research on Flight Path Planning Under Complex Environmental Constraints[J]. Air & Space Defense, 2024, 7(4): 99-105. |
| [4] |
LIU Yuanyuan, ZHOU Leimei, LI Hao, GAO Ziyi. Review of Formation Flight Control Methods for Unmanned Autonomous Air Combat[J]. Air & Space Defense, 2024, 7(4): 47-58. |
| [5] |
QUAN Jiale, MA Xianlong, SHEN Yuheng. Multi-agent Formation Method Based on Dynamic Optimization of Proximal Policies[J]. Air & Space Defense, 2024, 7(2): 52-62. |
| [6] |
MA Chi, ZHANG Guoqun, SUN Junge, LYU Guangzhe, ZHANG Tao. Deep Reinforcement Learning-Based Reconfiguration Method for Integrated Electronic Systems[J]. Air & Space Defense, 2024, 7(1): 63-70. |
| [7] |
LI Zheng, CHEN Jianwei, PENG Bo . UAV Cluster Path Planning Based on Pseudo-spectral Method[J]. Air & Space Defense, 2021, 4(1): 52-59. |
| [8] |
Wang Zhe, Xu Lingfei, Gu Cunfeng. Simulation of Damage Effect of Laser Weapon System Against Composite Unmanned Aerial Vehicle[J]. Air & Space Defense, 2018, 1(1): 63-68. |
|
|
|
|
2025, Vol. 8 
