Research On Robotic Arm Grasping System Based On Target Detection And Image Processing

In the research field of robot grasping,it is the key to generate the grasping posture of the grasping object.The method of producing stable holding posture in different situations is the central problem in this field.In this paper,was proposed a grasping position calculation method for two finger end chucks by applying a target detection algorithm based on the deep learning theory,Faster R-CNN to a grasping detection clause,and using closed thinking of digital image processing and trapping force,and finally the actual grasping.The grasping effect of the entire system was verified by the experiment.This paper determines the position of the object to be captured using the target recognition algorithm Faster R-CNN and the network structure and loss function analysed.On this basis,the offline training of fruit data sets using a deep learning framework Tensor Flow-GPU.The general network performance after offline training is checked by the test data set.The image matching method of Kinect V2.0 sensor based onpinhole camera model is established.According to the mathematical model of two finger end gripper grasping,a grasping pose calculation method for two finger end gripper is proposed by using digital image processing idea.This method can generate the grasping pose of the object to be grasped under the principle of force closure.The hand-eye calibration theory is analysed and the mapping relationship between the camera coordinate system and the robot base coordinate system is constructed using the least quadratic method;the robot's forward kinetic model is constructed on the basis of the five freedom manipulators,the constructed model is demonstrated by simulation experiments.The experimental platform is built,the results of different subsystems are combined,and the feasibility and effectiveness of the entire system are checked by the actual grip experiment.