DogSurf: 視覚障害者ナビゲーションのためのGRUベース表面認識が可能な四足歩行ロボット
DogSurf: Quadruped Robot Capable of GRU-based Surface Recognition for Blind Person Navigation
四足歩行ロボットを用いて視覚障害者のナビゲーションを支援するDogSurfを提案。GRUベースのニューラルネットワークで滑りやすい路面を検出し、音声と触覚フィードバックでユーザーに停止を促す。
著者: Artem Bazhenov, Vladimir Berman, Sergei Satsevich, Olga Shalopanova, Miguel Altamirano Cabrera, Artem Lykov, Dzmitry Tsetserukou
分類: cs.RO, cs.CV, cs.LG
原文アブストラクト
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.
