基于合并CNN框架的机器人避障方法

2019 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2019-07-01 DOI:10.1109/ICMLC48188.2019.8949168

Nai-Hsiang Chang, Yi-Hsing Chien, H. Chiang, Wei-Yen Wang, C. Hsu

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引用次数: 7

摘要

本文提出了一种用于自动避障的合并卷积神经网络(CNN)框架。虽然避障方法很多，但以往的避障方法大多能耗高、成本高。本文旨在利用单目网络摄像头实现一种基于图像的方法。实验结果表明，该方法可以有效地避开移动机器人导航中的障碍物。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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A Robot Obstacle Avoidance Method Using Merged CNN Framework

In this paper, a merged convolution neural network (CNN) framework is proposed to automatically avoid obstacles. Although there are many methods for avoiding obstacles, previous methods mostly contain high energy-consuming and high cost. This paper aims to realize an image-based method with a monocular webcam. The experimental results illustrate that the proposed method can effectively avoid obstacles in mobile robot navigation.

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来源期刊

2019 International Conference on Machine Learning and Cybernetics (ICMLC)

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