An Automatic Ground Collision Avoidance System with Reinforcement Learning
Fuente:
arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866908995384508416 |
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| author | Sevgili, Seyyid Osman Cilan, Atahan Demir, Mahir Yürütken, Özgün Can Bekar, Ümit Can |
| author_facet | Sevgili, Seyyid Osman Cilan, Atahan Demir, Mahir Yürütken, Özgün Can Bekar, Ümit Can |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_24403 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | An Automatic Ground Collision Avoidance System with Reinforcement Learning Sevgili, Seyyid Osman Cilan, Atahan Demir, Mahir Yürütken, Özgün Can Bekar, Ümit Can Machine Learning Robotics I.2.6; I.2.8; I.2.9; J.7 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. |
| title | An Automatic Ground Collision Avoidance System with Reinforcement Learning |
| topic | Machine Learning Robotics I.2.6; I.2.8; I.2.9; J.7 |
| url | https://arxiv.org/abs/2604.24403 |