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.
@article{arxiv.2402.03156,
title = {DogSurf: Quadruped Robot Capable of GRU-based Surface Recognition for Blind Person Navigation},
author = {Artem Bazhenov and Vladimir Berman and Sergei Satsevich and Olga Shalopanova and Miguel Altamirano Cabrera and Artem Lykov and Dzmitry Tsetserukou},
journal= {arXiv preprint arXiv:2402.03156},
year = {2024}
}
Comments
This paper has been accepted for publication at the HRI2024 conference