Autonomous fixed-wing interception using reinforcement learning
Autonomous fixed-wing interception using reinforcement learning
This paper introduces an AI-controlled fixed-wing interceptor capable of autonomously engaging and intercepting maneuvering targets. The system utilizes a neural network policy trained via proximal policy optimization to map monocular vision inputs directly to actuator commands. Validated in both simulation and real-world flight tests, the approach demonstrates superior performance over classical guidance controllers under most conditions, offering a potential low-cost alternative to expensive conventional air defense systems for reaching high-altitude targets.
The authors present a reinforcement learning-based control policy for autonomous fixed-wing drone interception, validated in both simulation and real flight against maneuvering targets.
Source: arXiv Robotics — research abstracts · Read original article ↗
Loading article text…
What the source reports
Publisher-reported claims, with original evidence. These results have not been independently verified by RoboSignal.
- Control
- Reported autonomousOpen source S5
- paper: not_reported
- code: not_reported
- weights: not_reported
- dataset: not_reported
Source excerpts and review record
Automatically extracted; no manual editorial approval recorded.
tor 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, th
Open source S4
e 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.
Open source S5
Source：arXiv Robotics — research abstracts · arxiv.org