Research On Target Recognition And Grasping Technology And System Development Of Vision Machine Arm

This paper studies the recognition of unknown irregular object outline,pose,and grasping area during the robot grasping process,so as to improve the robot's intelligence level.Based on the analysis of the current situation at home and abroad,combined with the deep learning method to carry out the work,for the autonomous recognition of the visual robot,the visual image acquisition and preprocessing work was first carried out.Based on the analysis of the working principle of the convolutional neural network,the FPN-based The multi-scale feature extraction model of the target detection task,and the introduction of the category imbalance loss(CI loss)method for the classification imbalance problem in the target detection task,has completed the optimized design of the target detection algorithm,and improved the accuracy of the object outline recognition.Speed and is suitable for unknown irregular objects;research unknown irregular object three-dimensional pose detection method,use full convolution pixel-level classification to increase network depth and improve detection accuracy;research complex scene target capture area analysis method,build multi-target capture Data set,without destroying the stacking state of objects,from the perspective of grasping area analysis to solve the current difficult to achieve and few researched multi-target grasping detection problems;aiming at the problem that the robot arm is susceptible to the interference of the external environment and some potential disturbance factors of the robot body when the visual robot is grasping small target objects,it is difficult for the robot to achieve high-precision control.Based on the analysis of the robot trajectory tracking control theory,designed a manipulator trajectory tracking controller based on RBF neural network,and carried out stability analysis,optimized the controller parameters through simulation test,and completed the rapid and stable high-precision trajectory tracking control of the manipulator.In the research process,the data set expansion and data processing of the three-dimensional digital model under the deep learning framework,the fusion of three-dimensional data features and prior knowledge in the deep learning network,the mathematical description of the hyperparameters of the three-dimensional digital model deep learning network and the optimization of training were refined.Key scientific issues,and conduct experimental research through the development of prototype systems.The results of this paper have application prospects in modern logistics,social services,emergency rescue and other fields,and provide theoretical foundations and technical solutions for scientific research and industrial applications of robotics.