強化学習を用いた自動対地衝突回避システム
An Automatic Ground Collision Avoidance System with Reinforcement Learning
ジェット練習機向けに、強化学習ベースの自動対地衝突回避システムを設計し、限られた観測空間での安全性と運用効率を向上させる手法を提案した論文。
著者: Seyyid Osman Sevgili, Atahan Cilan, Mahir Demir, Özgün Can Yürütken, Ümit Can Bekar
分類: cs.LG, cs.RO
原文アブストラクト
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.