Stair Climbing Robot Based on Convolutional Neural Networks for Visual Impaired

Guillermo Campos, David Poza, M. Reyes, Alma Zacate, Hiram Ponce, J. Brieva, E. Moya-Albor
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引用次数: 3

Abstract

When a person loses the sense of sight, in general, it is suggested to use a white cane to perform daily activities. However, using a white cane limits the movement of a person. In addition, guide dogs can be served in this impairment. However, the acquisition and maintenance of a guide dog is extremely high for people in development countries. In this regard, this paper presents a proof-of-concept of a low-cost robotic system able to guide a visual impaired, as a guide dog. The robot is specially designed for climbing stairs at indoors, and it uses convolutional neural networks (CNN) for both object detection and hand gesture recognition for special instructions from the user. Experimental results showed that our prototype robot can climb stairs with 86.7% of efficiency in concrete stair surfaces. Also, the visual representation by CNN performed more than 98% accuracy.
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基于卷积神经网络的视障爬楼梯机器人
当一个人失去视觉时,一般建议使用白色手杖进行日常活动。然而,使用白色手杖限制了人的活动。此外,导盲犬可以在这种情况下提供服务。然而,对于发展中国家的人们来说,购买和维护导盲犬的费用非常高。在这方面,本文提出了一个低成本的机器人系统的概念验证,能够引导视障人士,作为导盲犬。该机器人是专门为在室内爬楼梯而设计的,它使用卷积神经网络(CNN)进行物体检测和手势识别,以满足用户的特殊指令。实验结果表明,该机器人在混凝土楼梯表面的爬楼梯效率为86.7%。此外,CNN的视觉呈现准确率超过98%。
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