Real-time Detection and Classification of Machine Parts with Embedded System for Industrial Robot Grasping

	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
会议名称	2015 IEEE International Conference on Mechatronics and Automation
会议录名称	Proceedings of 2015 IEEE International Conference on Mechatronics and Automation
页码	1691-1696
会议日期	August 2-5, 2015
会议地点	Beijing, China
摘要	In 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.
关键词	Machine Vision Object Recognition Fpga Embedded System Industrial Robot
收录类别	EI
语种	英语
文献类型	会议论文
条目标识符	http://ir.ia.ac.cn/handle/173211/10350
专题	智能制造技术与系统研究中心_智能机器人
作者单位	中国科学院自动化研究所
第一作者单位	中国科学院自动化研究所
推荐引用方式 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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