تقرير
DogSurf: Quadruped Robot Capable of GRU-based Surface Recognition for Blind Person Navigation
| العنوان: | DogSurf: Quadruped Robot Capable of GRU-based Surface Recognition for Blind Person Navigation |
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| المؤلفون: | Bazhenov, Artem, Berman, Vladimir, Satsevich, Sergei, Shalopanova, Olga, Cabrera, Miguel Altamirano, Lykov, Artem, Tsetserukou, Dzmitry |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Robotics, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning |
| الوصف: | This paper introduces DogSurf - a newapproach of using quadruped robots to help visually impaired people navigate in real world. The presented method allows the quadruped robot to detect slippery surfaces, and to use audio and haptic feedback to inform the user when to stop. A state-of-the-art GRU-based neural network architecture with mean accuracy of 99.925% was proposed for the task of multiclass surface classification for quadruped robots. A dataset was collected on a Unitree Go1 Edu robot. The dataset and code have been posted to the public domain. Comment: This paper has been accepted for publication at the HRI2024 conference |
| نوع الوثيقة: | Working Paper |
| DOI: | 10.1145/3610978.3640606 |
| URL الوصول: | http://arxiv.org/abs/2402.03156 |
| رقم الأكسشن: | edsarx.2402.03156 |
| قاعدة البيانات: | arXiv |
| DOI: | 10.1145/3610978.3640606 |
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