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DogSurf: Quadruped Robot Capable of GRU-based Surface Recognition for Blind Person Navigation

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arxiv 2402.03156 v1 pith:AFXXIQU2 submitted 2024-02-05 cs.RO cs.CVcs.LG

Artem Bazhenov , Vladimir Berman , Sergei Satsevich , Olga Shalopanova , Miguel Altamirano Cabrera , Artem Lykov , Dzmitry Tsetserukou This is my paper · ORCID
classification cs.ROcs.CVcs.LG
keywords quadrupedrobotdatasetdogsurfgru-basedrobotssurfaceaccuracy
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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.

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