Concerning the problem that automobile part recycling factories cannot achieve accurate grasping and thus affects production efficiency due to poor part detection under actual complex working conditions, a robotic grasping system based on improved Single Shot multibox Detector (SSD) algorithm was proposed to realize the tasks of part detection, classification, location and grasping, including detection, location and grasping functions of the target parts. First, the target parts were detected by the improved SSD model, obtaining the part location and class information. Second, through Kinect camera calibration and hand-eye calibration, the pixel coordinate system was transferred into robot world coordinate system to realize the location of parts in robot spatial coordinate system. Third, the target part grasping task was completed by robot positive and inverse kinematic modeling and trajectory planning. Finally, the validation experiments of the whole integrated system on part detection, classification, location and grasping were carried out. Experimental results show that under complex working conditions, the average part grasping success rate of the proposed system reaches 95%, which meets the actual production demand of part grasping.

For traditional SURF (Speeded-Up Robust Feature) algorithm takes a long time for constructing local feature descriptors, an improved SURF algorithm was proposed to meet the real-time requirement. Firstly, the Determinant of Hessian (DoH) matrix was adopted to detect the key points of an image. Non-maximum suppression algorithm and interpolation operation were used to search and position the extreme points. Secondly, gray centroid method was adopted to determine the main direction of the key points. Then a binary descriptor, BRIEF (Binary Robust Independent Elementary Feature), was adopted to describe the key points, and the main direction of the key points was used to construct a directed feature descriptor with the objective of keeping its rotation invariance. Finally, Hamming distance was used to preliminarily determine the matching points. Then, the mismatching points were removed to improve the matching accuracy by ratio detection method and RANSAC (Random Sample Consensus) algorithm. The experimental results show that, when the improved SURF algorithm is applied to the flexible clamping robot, the matching time is reduced from 214.10 ms to 86.29 ms, the matching accuracy is increased by 2.6% compared with traditional SURF algorithm. Therefore, the proposed algorithm can improve the workpiece image matching speed and matching precision of flexible clamping robot effectively.