Research On Motion Control Technology Of Robotic Arm Based On OpenCV

Compared with traditional manual handling,the manipulator has the advantages of high production efficiency,fatigue resistance,high precision,good stability,small working space and low cost,which greatly improves the production efficiency.Manipulator as a highly complex nonlinear system with time-varying coupling,This thesis focuses on the two problems of uncertain parameters and visual target positioning,optimizes the joint angle parameters through intelligent algorithm to ensure that the manipulator can follow the tracks of target objects,and combines with image processing technology to realize the sorting and grasping of the target objects.The main achievements of this thesis are as follows:Firstly,the kinematics model of the manipulator with 6 degrees of freedom is established.The coordinate transformation relationship between the links in the moving body is studied by using D-H(Denavit Hartenberg)parameters,and the forward kinematics model and inverse kinematics model of the manipulator are analyzed.Secondly,in view of the complex background environment,combined with the existing machine vision technology,this paper proposes an object detection optimization algorithm.Firstly,the image is preprocessed to realize the separation of background and foreground,and then the target object in the foreground image is determined by homography matrix combining template matching and SIFT feature matching;Secondly,according to the relationship between the coordinates,the algorithm obtains the three-dimensional coordinate system.Finally,compared with the other three target detection algorithms,and the results show that the proposed optimization algorithm has better performance in terms of the accuracy and the recall;in the camera calibration experiment,the internal parameters of the camera are obtained,and the error with the actual is small.Thirdly,in order to solve the problem of multiple solutions and high computational complexity in inverse kinematics calculation of each joint angle,a multi colony ant algorithm(MCAA)is proposed to optimize the trajectory of the manipulator.According to the machine vision technology,the coordinates of the target object are detected,and the angle range of each joint is calculated by using inverse kinematics.The angle range of each joint is taken as the input parameter of the algorithm,and the angle of each joint is abstracted as ant.The optimal angle of each joint is evolved by using pre selection and interaction mechanism.Besides in order to ensure that the manipulator can run according to the expected trajectory calculated by multi colony ant algorithm,this thesis uses iterative learning control method in the process of system operation,which reduces the error between the expected trajectory and the actual trajectory.The simulation results show that: compared with the improved ant colony algorithm and the improved multi colony integrated differential evolution algorithm,this algorithm has better performance advantages in grasping time and accuracy.Fourthly,according to the relevant theory and algorithm analysis,this thesis builds the corresponding prototype experiment,and verifies the correctness of the relevant algorithm of manipulator motion control.