The obstacles detection for outdoor robot based on computer vision in deep learning

Hsuan Chen, Wen Hsin Chiu, Jian Cheng Yu, Hsiang Chieh Chen, Wen June Wang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

In this thesis, we propose a guided robot with deep learning techniques and machine vision in order to predict distance and avoid obstacles. The training data images are captured by the stereo camera, and output results are disparity of the single image. Then, the distances are converted by using the triangulation method of computer vision. The back propagation neural network retrains to obtain the actual distance of each pixel in the image. Therefore, the robot with the monocular camera could know the distance between obstacles and itself. Semantic segmentation is utilized to a to distinguish road and obstacles in the image. Fuzzy theory for calculating the area of the road which be cut is designed to avoid walking into the intersection. The obstacle depth and the walkable area are taken as an information for avoiding obstacles control. Because of the robot is proposed to walk on the right side along the road, the edge of road is necessary to navigate the robot. The Hough method is used to find the straight line, and then choose the line we need. The path planning is also achieved in through of navigation and obstacle avoidance control. As a result, the robot can arrive the destination safely and precisely.

Original languageEnglish
Title of host publicationProceedings - 2019 IEEE 9th International Conference on Consumer Electronics, ICCE-Berlin 2019
EditorsGordan Velikic, Christian Gross
PublisherIEEE Computer Society
Pages184-188
Number of pages5
ISBN (Electronic)9781728127453
DOIs
StatePublished - Sep 2019
Event9th IEEE International Conference on Consumer Electronics, ICCE-Berlin 2019 - Berlin, Germany
Duration: 8 Sep 201911 Sep 2019

Publication series

NameIEEE International Conference on Consumer Electronics - Berlin, ICCE-Berlin
Volume2019-September
ISSN (Print)2166-6814
ISSN (Electronic)2166-6822

Conference

Conference9th IEEE International Conference on Consumer Electronics, ICCE-Berlin 2019
Country/TerritoryGermany
CityBerlin
Period8/09/1911/09/19

Keywords

  • Computer vision
  • Deep learning
  • Fuzzy control
  • Robot navigation

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