Path Planning using Model Predictive Controller based on Potential Field for Autonomous Vehicles.
Yazarlar (2)
Dr. Öğr. Üyesi Zehra ELMI Hacettepe Üniversitesi, Türkiye
Dr. Öğr. Üyesi Mehmet Önder Hacettepe Üniversitesi, Türkiye
| Bildiri Türü |
Tebliğ/Bildiri
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Bildiri Dili | İngilizce |
| Bildiri Alt Türü | Tam Metin Olarak Yayınlanan Tebliğ (Uluslararası Kongre/Sempozyum) | ||
| Bildiri Niteliği | Web of Science Kapsamındaki Kongre/Sempozyum | ||
| DOI Numarası | 10.1109/IECON.2018.8591282 | ||
| Kongre Adı | IECON 2018-44th Annual Conference of the IEEE Industrial Electronics Society | ||
| Kongre Tarihi | 21-12-2018 / 23-12-2018 | ||
| Basıldığı Ülke | Amerika Birleşik Devletleri | Basıldığı Şehir | |
| Bildiri Linki | https://ieeexplore.ieee.org/abstract/document/8591282 | ||
| UAK Araştırma Alanları |
Yapay Zeka
Robotik
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| Özet |
| In recent decades, one of the challenging problems is path planning for autonomous vehicle in dynamic environments with along static or moving obstacles. The main aim of these researches is to reduce congestion, accidents and improve safety. We propose an optimal path planning using model predictive controller (MPC) which automatically decides about the mode of maneuvers such as lane keeping and lane changing. For ensuring safety, we have additionally used two different potential field functions for road boundary and obstacles where the road potential field keeps the vehicle for going out of the road boundary and the obstacle potential field keep the vehicle away from obstacles. We have tested the proposed path planning on the different scenarios. The obtained results represent that the proposed method is effective and makes reasonable decision for different maneuvers by observing road regulations … |
| Anahtar Kelimeler |
| Artificial Potential Field | Autonomous Vehicles | Model Predictive Control | Path Planning | Sequential Quadratic Programming |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Scopus | 11 |
| Google Scholar | 15 |