Visual-based obstacle avoidance method using advanced CNN for mobile robots
Yazarlar (2)
Bursa Teknik Üniversitesi, Türkiye
Arş. Gör. Muhammed ÇELİK
Tokat Gaziosmanpaşa Üniversitesi, Türkiye
| Makale Türü | Özgün Makale |
| Makale Alt Türü | SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale |
| Dergi Adı | Internet of Things the Netherlands |
| Dergi ISSN | 2542-6605 Wos Dergi Scopus Dergi |
| Dergi Tarandığı Indeksler | SCI-Expanded |
| Dergi Grubu | Q1 |
| Makale Dili | İngilizce |
| Basım Tarihi | 05-2025 |
| Cilt No | 31 |
| Sayı | 1 |
| Sayfalar | 1 / 27 |
| DOI Numarası | 10.1016/j.iot.2025.101538 |
| Makale Linki | https://doi.org/10.1016/j.iot.2025.101538 |
| Özet |
| Artificial intelligence is one of the key factors accelerating the development of cyber-physical systems. Autonomous robots, in particular, heavily rely on deep learning technologies for sensing and interpreting their environments. In this context, this paper presents an extended MobileNetV2-based obstacle avoidance method for mobile robots. The deep network architecture used in the proposed method has a low number of parameters, making it suitable for deployment on mobile devices that do not require high computational power. To implement the proposed method, a two-wheeled non-holonomic mobile robot was designed. This mobile robot was equipped with a Jetson Nano development board to utilize deep network architectures. Additionally, camera and ultrasonic sensor data were used to enable the mobile robot to detect obstacles. To test the performance of the proposed method, three different obstacle ... |
| Anahtar Kelimeler |
| Cyber-physical systems | Deep learning | Mobile robots |
Science Direct
Visual-based obstacle avoidance method using advanced CNN for mobile robots
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| WoS | 2 |
| SCOPUS | 5 |
| Google Scholar | 9 |