Intelligent Selection of Mobility Systems For Unmanned Ground Vehicles Through Machine Learning
Yazarlar (1)
Doç. Dr. Merdin DANIŞMAZ Kırşehir Ahi Evran Üniversitesi, Türkiye
| Makale Türü | Özgün Makale (Uluslararası alan indekslerindeki dergilerde yayınlanan tam makale) | ||
| Dergi Adı | Revista de Gestão Social e Ambiental | ||
| Makale Dili | – | Basım Tarihi | 09-2025 |
| Cilt / Sayı / Sayfa | 19 / 3 / 1–21 | DOI | – |
| Makale Linki | https://search.proquest.com/openview/77c5812f43531d5795fbb1763ac5a773/1?pq-origsite=gscholar&cbl=2031968 | ||
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Isı Transferi
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| Özet |
| Objective The primary objective of this study is to enhance the selection process of mobility systems for unmanned ground vehicles (UGVs) by leveraging machine learning techniques. Specifically, it aims to identify the most suitable mobility systems that align with mission requirements and user needs while optimizing performance across diverse terrains. Theoretical Framework This research is grounded in theories of systems engineering and decision-making processes related to vehicle design. It builds on the premise that mobility systems are key determinants of vehicle performance, affecting aspects such as energy efficiency, maneuverability, and load-carrying capacity. The integration of machine learning within the design process represents a shift from traditional methodologies, facilitating a data-driven approach to system selection. Method The study employed a machine learning framework to analyze UGV … |
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