Intelligent grasping system for industrial robot based on monocular vision
                                                         ZHANG Chi, LIAO Hua li, ZHOU Jun
                         （College of Mechanical and Electrical, Hohai University, Changzhou 213022, China）

Abstract: Aiming at the problem that an industrial robot how to quickly and autonomously identify and capture a specified target artifact under multi target conditions, monocular vision was applied to the design of an intelligent grasping system for industrial robots.In order to establish the relationship between the visual image and the workpiece positioning and grapping, the pattern recognition, detection and location using images were studied, and a monocular vision capture system based on contour Hu invariant moments fast template matching algorithm was proposed. Firstly, the image data collected from the camera should be preprocessed, then the target was identified by the template matching method based on the contour Hu invariant moment, and the position of the target workpiece was obtained by using the contour moment and the principle of secondorder moment of inertia. Finally, through the establishment of SOCKET communication, the desired pose was sent to the manipulator control system to guide the manipulator to grasp.Based on VS software development platform and ABB robot , the intelligent grasping system was built and test. The results indicate that the industrial robot intelligent grasping system based on monocular vision has low cost, high positioning accuracy and meets the needs of industrial automation production.

Key words: monocular vision; industrial robot; image processing; template matching; pose detection