An Automatic Ground Collision Avoidance System with Reinforcement Learning

TL;DR

This paper presents an AI-based automatic ground collision avoidance system for jet trainers, utilizing reinforcement learning and terrain data to improve safety and operational efficiency.

Contribution

It introduces a novel AI-driven AGCAS tailored for jet trainers, emphasizing terrain-aware collision avoidance within limited observation spaces.

Findings

System effectively uses line-of-sight terrain queries for collision avoidance.

Enhances safety and operational capabilities of jet trainers.

Addresses AGCAS problem with limited observation data.

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.