Research On Intelligent Grabbing System Of Manipulator Based On Deep Learning

With the continuous expansion of robot application field,robot intelligent control technology has become increasingly short board.The commonly used robot control mode is mainly through the teaching device or offline programming method,which is fixed point-to-point control mode.It can not cope with the complex grasping environment,and the robot needs to have higher intelligence In addition,the traditional robot grasping is targeted at a specific test artifacts,through the artificial design feature extraction,and use the template matching method,etc This kind of method of portability,low robustness Therefore,in the face of diverse categories Of grasping object,this topic put forward a set of the ROS software environment,based on the deep study of the mechanical arm intelligent fetching system,able to cope with a variety of mechanical arm to grab the environment,improve the ability of intelligent mechanical arm crawl(1)First of all,the overall demand analysis of the system is carried out for the daily necessities to be captured,and the overall framework of the system is built to design the hardware selection and system control process(2)Single target recognition algorithm based on image information sources,lead to small and medium-sized target leakage inspection error phenomenon,put forward the improved multilayer scale feature fusion algorithm,rich features layer information,enhanced features figure expression ability In addition,in order to further improve the detection accuracy of the algorithm,the visual sensor calibration,ensure that the information in the space field completely mapped to the image(3)Aiming at the fast judgment of the pose of the detected object,based on the accurate target object category and positioning box,the object point cloud model is obtained by clustering algorithm,and the representative geometric feature points are extracted by RANSAC algorithm.Finally,the principal direction of the target point cloud is judged by PCA,and the pose information of the target object is obtained.Combined with the location information of the positioning box,the pose of the target object is obtained(4)In view of the actual working situation of the manipulator,the quick response of the manipulator and the safe range of the manipulator motion should be considered.Therefore,RRT algorithm with random search path point strategy is improved.The core idea is to propose an improved RRT algorithm combined with artificial potential field method and load it into OMPL motion planning library,which can effectively save path search time and improve the success rate of robot arm motion planning in high dimensional space.(5)Aiming at the problems of target detection algorithm,target pose judgment algorithm,motion planning algorithm and manipulator control logic,based on the ROS robot operating system,the overall software and hardware of the system are integrated,and the capture situation is analyzed by using Move It motion planning plug-in and rviz visualization software.Finally,the pose of the target in the vision sensor is transformed to the manipulator coordinate system through coordinate transformation,and finally the target is successfully captured.In this paper,the robot intelligent grasping system based on deep learning is built.On the basis of improving the traditional target detection algorithm for false detection and missing detection,it effectively improves the robot’s grasping ability in complex environment.The experimental results show that the manipulator gripper system proposed in this paper has high operability and feasibility.