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Real-time Detection and Classification of Machine Parts with Embedded System for Industrial Robot Grasping
Hao Guo; Han Xiao; Shijun Wang; Wenhao He; Kui Yuan
2015-08
Conference Name2015 IEEE International Conference on Mechatronics and Automation
Source PublicationProceedings of 2015 IEEE International Conference on Mechatronics and Automation
Pages1691-1696
Conference DateAugust 2-5, 2015
Conference PlaceBeijing, China
AbstractIn this paper, a real-time machine vision system is designed for an industrial robot to grasp from an assembly line a class of machine parts which are similar in the general shape but different in details. In order to get real-time performance, the system is implemented on an embedded image card with an FPGA (Field Programming Gate Array) accelerating the computation. The method can be divided into two stages. First, the holes and edges of the machine parts are detected from each frame with the FPGA. Then a DSP (Digital Signal Processor) chip on the image card performs the rest of the computation by identifying the location and type of each of the machine parts in the image based on the information of all the holes and edges. A rotationally adaptive edge-based template matching technique is used in our method, which not only reduces the amount of computation but also provides robustness against illumination changes. Experiments demonstate that the machine parts can be located accurately under arbitrary in-plane rotations and can be classified correctly according to the details in their shapes. Our system can run with an industrial camera at a resolution of 640×480 and a speed of 50 fps (frames per second) or higher.
KeywordMachine Vision Object Recognition Fpga Embedded System Industrial Robot
Indexed ByEI
Language英语
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/10350
Collection智能制造技术与系统研究中心_智能机器人
Affiliation中国科学院自动化研究所
Recommended Citation
GB/T 7714
Hao Guo,Han Xiao,Shijun Wang,et al. Real-time Detection and Classification of Machine Parts with Embedded System for Industrial Robot Grasping[C],2015:1691-1696.
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