用强化学习打造智能防撞系统，提升高级教练机飞行安全
An Automatic Ground Collision Avoidance System with Reinforcement Learning
- 基于强化学习设计防撞系统，利用视线查询地形数据
- 在有限观测空间下实现精准高效避障决策
- 适合关注智能飞行安全的航空航天研究者
本文评估了一种基于人工智能（AI）的自动地面碰撞规避系统（AGCAS），专为高级喷气式教练机设计，旨在提升作战效能。在航空航天工程不断发展的背景下，人工智能的融合对提高操作时效性和效率至关重要。本研究探讨了面向高级喷气式教练机的AI驱动型AGCAS的设计过程，重点解决在有限观测空间下的碰撞规避问题。系统通过向地形服务器发起视线查询，确保碰撞规避的精确性与高效性。该方法显著提升了高级教练机的安全性与操作能力。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。