An Automatic Ground Collision Avoidance System with Reinforcement Learning

arXiv cs.RO / 4/28/2026

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Key Points

The paper presents an AI-based Automatic Ground Collision Avoidance System (AGCAS) for advanced jet trainers, aiming to improve safety and operational effectiveness under tighter timing constraints.
It describes designing an AI-driven AGCAS to handle the AGCAS problem using a limited observation space, making the approach more practical for constrained sensing scenarios.
The proposed system uses reinforcement learning and line-of-sight queries to a terrain server to achieve precise and efficient collision avoidance.
The study frames the work as a step toward integrating AI into aerospace operations, emphasizing improved collision avoidance capabilities for jet training platforms.

Abstract

This article evaluates an artificial intelligence (AI)-based Automatic Ground Collision Avoidance System (AGCAS) designed for advanced jet trainers to enhance operational effectiveness. In the continuously evolving field of aerospace engineering, the integration of AI is crucial for advancing operations with improved timing constraints and efficiency. Our study explores the design process of an AI-driven AGCAS, specifically tailored for advanced jet trainers, focusing on addressing the AGCAS problem within a limited observation space. The system utilizes line-of-sight queries on a terrain server to ensure precise and efficient collision avoidance. This approach aims to significantly improve the safety and operational capabilities of advanced jet trainers.