TY - GEN
T1 - A Robot Obstacle Avoidance Method Using Merged CNN Framework
AU - Chang, Nai Hsiang
AU - Chien, Yi Hsing
AU - Chiang, Hsin Han
AU - Wang, Wei Yen
AU - Hsu, Chen Chien
N1 - Funding Information:
This work was financially supported by the “Chinese Language and Technology Center” of National Taiwan
Funding Information:
This work was financially supported by the Chinese Language and Technology Center of National Taiwan Normal University (NTNU)
Funding Information:
Normal University (NTNU) from The Featured Areas Research Center Program within the framework of the Higher Education Sprout Project by the Ministry of Education (MOE) in Taiwan, and Ministry of Science and Technology, Taiwan, under Grants no. MOST 108-2634-F-003-002 and MOST 108-2634-F-003-003 through Pervasive Artificial Intelligence Research (PAIR) Labs. We are grateful to the National Center for High-performance Computing for computer time and facilities to conduct this research.
Publisher Copyright:
© 2019 IEEE.
PY - 2019/7
Y1 - 2019/7
N2 - 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.
AB - 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.
KW - Merged CNN framework
KW - Obstacle avoidance method
KW - ROS architecture
UR - http://www.scopus.com/inward/record.url?scp=85072919948&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85072919948&partnerID=8YFLogxK
U2 - 10.1109/ICMLC48188.2019.8949168
DO - 10.1109/ICMLC48188.2019.8949168
M3 - Conference contribution
AN - SCOPUS:85072919948
T3 - Proceedings - International Conference on Machine Learning and Cybernetics
BT - Proceedings of 2019 International Conference on Machine Learning and Cybernetics, ICMLC 2019
PB - IEEE Computer Society
T2 - 18th International Conference on Machine Learning and Cybernetics, ICMLC 2019
Y2 - 7 July 2019 through 10 July 2019
ER -