arXiv cs.AI· Daan van Hamersveld, Ewoud Smeur, Iurii Kriachko·· 13 小时前AI 评分48
强化学习实现固定翼无人机自主拦截
Autonomous fixed-wing interception using reinforcement learning
AI 导读
荷兰代尔夫特理工大学的一项硕士研究提出用强化学习训练的神经网络控制固定翼拦截无人机，将滤波后的单目视觉观测直接映射为执行器指令，策略在自定义 Python 环境中用 PPO 训练后部署到真实固定翼平台。仿真中该策略在多数条件下优于两种经典制导控制器，并在真实飞行中成功拦截机动固定翼目标。
正文 · 原文
Abstract:Medium altitude, long endurance unmanned aerial systems such as Shahed-type strike drones are an unanswered threat in modern conflicts. Conventional air defense systems are too expensive to provide a feasible large-scale solution. Quadcopter interceptors are a cheap, battle-proven alternative, but their extremely limited flight time restricts them to low altitude targets at short range, whereas fixed-wing platforms have the endurance to reach higher altitude targets from longer distances. However, such interceptors are still operated manually. Recent developments in learning-based controllers show potential for automation. In this work, a fixed-wing interceptor is presented that is controlled by a neural network that maps filtered monocular vision observations directly to actuator commands. The policy was trained in a custom Python environment using reinforcement learning and proximal policy optimization. The policy was then deployed on a fixed-wing platform and tested in real interception scenarios. As a baseline, two classical guidance controllers were also developed and tested in simulation. The policy is able to intercept targets both in simulation and in real flight. In simulation, the policy outperforms the classical controllers under most conditions. The interceptor was ultimately successfully validated in real flight by intercepting maneuvering fixed-wing targets. This novel system shows the potential of artificial intelligence for autonomous control of fixed-wing interceptor drones.
| Comments: | MSc thesis, Faculty of Aerospace Engineering, Delft University of Technology, 2026. 24 pages, 29 figures, 4 tables |
| Subjects: | Robotics (cs.RO); Artificial Intelligence (cs.AI); Neural and Evolutionary Computing (cs.NE); Systems and Control (eess.SY) |
| Cite as: | arXiv:2610.37470 [cs.RO] |
| (or arXiv:2610.37470v1 [cs.RO] for this version) | |
| https://doi.org/10.48550/arXiv.2610.37470 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Daan Van Hamersveld [view email]
[v1]
Tue, 27 Oct 2026 16:04:06 UTC (21,543 KB)
来源：arXiv cs.AI · arxiv.org