TY - GEN
T1 - Machine Vision Based Autonomous Loading Perception for Super-huge Mining Excavator
AU - Li, Yunhua
AU - Niu, Tianhao
AU - Qin, Tao
AU - Yang, Liman
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021/8/1
Y1 - 2021/8/1
N2 - The super-huge mining excavator has a giant structure of work devices and harsh working conditions, which results in a limited view of the operator. Therefore, how to assist the operator in determining the relative position of the bucket and the dump truck during the excavator loading operations has become a prominent issue. To solve this problem, this paper proposes a relative position perception and correction scheme based on machine vision technology. First, the Scale Invariant Feature Transform (SIFT) is used to recognize the dump truck target in the image captured by the camera. Then, the positioning algorithm based on color detection is used to identify and analyze the markers on the dump truck. Finally, through simulation tests, the proposed scheme can accurately judge the relative position of the bucket and the dump truck, and give the excavator a suitable rotation signal. The research is of great significance to the development of unmanned and intelligent excavators.
AB - The super-huge mining excavator has a giant structure of work devices and harsh working conditions, which results in a limited view of the operator. Therefore, how to assist the operator in determining the relative position of the bucket and the dump truck during the excavator loading operations has become a prominent issue. To solve this problem, this paper proposes a relative position perception and correction scheme based on machine vision technology. First, the Scale Invariant Feature Transform (SIFT) is used to recognize the dump truck target in the image captured by the camera. Then, the positioning algorithm based on color detection is used to identify and analyze the markers on the dump truck. Finally, through simulation tests, the proposed scheme can accurately judge the relative position of the bucket and the dump truck, and give the excavator a suitable rotation signal. The research is of great significance to the development of unmanned and intelligent excavators.
KW - autonomous loading perception
KW - machine vision
KW - position detection
KW - super-huge excavator
UR - https://www.scopus.com/pages/publications/85115448650
U2 - 10.1109/ICIEA51954.2021.9516320
DO - 10.1109/ICIEA51954.2021.9516320
M3 - 会议稿件
AN - SCOPUS:85115448650
T3 - Proceedings of the 16th IEEE Conference on Industrial Electronics and Applications, ICIEA 2021
SP - 1250
EP - 1255
BT - Proceedings of the 16th IEEE Conference on Industrial Electronics and Applications, ICIEA 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 16th IEEE Conference on Industrial Electronics and Applications, ICIEA 2021
Y2 - 1 August 2021 through 4 August 2021
ER -