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arXiv AI By Daan van Hamersveld, Ewoud Smeur, Iurii Kriachko

Autonomous fixed-wing interception using reinforcement learning

Read the original on arXiv AI →

The paper presents a fixed‑wing interceptor drone that uses a neural network trained with reinforcement learning to map monocular vision inputs directly to actuator commands. The policy, developed in a custom Python environment with proximal policy optimization, was tested against classical guidance controllers and demonstrated superior performance in simulation and successful interception of maneuvering fixed‑wing targets in real flight. This work illustrates the feasibility of AI‑driven autonomous control for long‑endurance interception missions.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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