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This paper presents a reinforcement learning-based Automatic Ground Collision Avoidance System (AGCAS) for advanced jet trainers, designed to improve operational effectiveness within limited observation spaces. The system uses line-of-sight queries on a terrain server to determine collision risk and guide avoidance maneuvers. Initial results suggest the AI-driven AGCAS can enhance the safety and operational capabilities of advanced jet trainers.
AI can now pilot jet trainers to avoid ground collisions, even with limited visibility.
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.
