A Robot Obstacle Avoidance Method Using Merged CNN Framework

Nai Hsiang Chang, Yi Hsing Chien, Hsin Han Chiang, Wei Yen Wang, Chen Chien Hsu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationProceedings of 2019 International Conference on Machine Learning and Cybernetics, ICMLC 2019
PublisherIEEE Computer Society
ISBN (Electronic)9781728128160
DOIs
Publication statusPublished - 2019 Jul
Event18th International Conference on Machine Learning and Cybernetics, ICMLC 2019 - Kobe, Japan
Duration: 2019 Jul 72019 Jul 10

Publication series

NameProceedings - International Conference on Machine Learning and Cybernetics
Volume2019-July
ISSN (Print)2160-133X
ISSN (Electronic)2160-1348

Conference

Conference18th International Conference on Machine Learning and Cybernetics, ICMLC 2019
CountryJapan
CityKobe
Period19/7/719/7/10

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Keywords

  • Merged CNN framework
  • Obstacle avoidance method
  • ROS architecture

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computational Theory and Mathematics
  • Computer Networks and Communications
  • Human-Computer Interaction

Cite this

Chang, N. H., Chien, Y. H., Chiang, H. H., Wang, W. Y., & Hsu, C. C. (2019). A Robot Obstacle Avoidance Method Using Merged CNN Framework. In Proceedings of 2019 International Conference on Machine Learning and Cybernetics, ICMLC 2019 [8949168] (Proceedings - International Conference on Machine Learning and Cybernetics; Vol. 2019-July). IEEE Computer Society. https://doi.org/10.1109/ICMLC48188.2019.8949168