Research On Manipulator Grasping System Based On Deep Learning Object Detection Algorithm

At present,due to the rapid development of science and technology,some industrial production lines are gradually developing to intelligentsia to reduce labor costs and improve labor efficiency,and adopting the mechanical arm as the most common task of intelligent research is very necessary.In this article,in order to achieve intelligent interception of the manipulator arm,a manipulator arm gripping system based on a deep learning object detection algorithm is designed for the manipulator cooperating with six degrees of freedom.The system can actively identify the target using an object detection algorithm and provide the position and path of the target for the manipulator to be captured.The main research content of this article presents the following aspects:(1)In order to detect captured objects,this article proposes a network structure based on the Single Shot Multibox Detector(SSD)object detection algorithm.Object maps obtained from the convolutional layers Conv3＿3 and Conv4＿3 in the VGG16 SSD core network have been combined to obtain more detailed information in the lower layer.The SENet network has been added at each stage to increase the importance of useful function channels.Test results show that the network can improve the accuracy of detecting small and medium targets.(2)For the target object’s 3D exposure information,firstly,the coordinate system conversion relationship was obtained by calibrating the D200 n depth camera,and the target image’s 2D image information was converted into 3D spatial information.Finally,you get information about the 3D placement of the target using the position conversion relationship.(3)In order to build a mechanical arm gripping platform,an experimental gripping platform was built with a Huiling Z-Arm-S622 collaborative robot arm with six degrees of freedom and a D200 n depth camera.The mapping of the target to the robot’s coordinate system was done using kinematic modeling and manipulator analysis,and the gripping task was completed with a trajectory of movement.Finally,the rationality of the input system is verified by input experiments.(4)In order to respond quickly to the manipulator in the input process,a trajectory planning method based on 3-5-3 polynomial interpolation has been adopted,and the particle swarm optimization algorithm(PSO)is used for time optimization.The effectiveness of this method is verified by comparing the simulation results.Thanks to the study of several main directions of the call,it was possible to build a robotic arm gripping system based on the algorithm for detecting deep learning objects.The results of the experiments lead to the conclusion that the system has high functionality and feasibility.