Optimization Of The Grinding Trajectory Of The Engine Piston Skirt Robot Based On Machine Vision

The piston skirt is designed to guide the piston for reciprocating motion in the pneumatic cylinder and bear the side pressure.To ensure that the piston skirt works stably under high temperature and pressure conditions,it is necessary to strictly control its processing.In the process of piston production,the grinding of the edge of the piston skirt mainly relies on manual labor,which has low efficiency and a harsh working environment,and seriously affects the health of workers.The use of robot grinding can not only liberate labor,but also ensure the consistency and stability of the surface of the polished parts.To improve the grinding efficiency of the robot and reduce the working energy consumption,the extraction and trajectory optimization of data points of the grinding path were carried out on the engine piston skirt based on machine vision in this thesis,and the main research contents of this thesis are as follows.(1)A monocular vision system for grinding paths was established.According to the imaging model of the camera and the working principle of Zhang’s calibration method,calibration experiments were completed in MATLAB,and internal and external parameters and the distortion coefficient of the camera were obtained.The hand-eye relationship matrix was obtained through the hand-eye calibration experiment,and the conversion formulas between the pixel coordinate system and the robot coordinate system were deduced;(2)The data points of the grinding path of the engine piston skirt were automatically extracted.Image preprocessing was done on the collected grinding path images.The edge detection operator was used to detect the contour of the grinding path,Harris corner detection was used to obtain the pixel coordinates of data points of the grinding path,and the conversion formula was used to convert the pixel coordinates of data points to three-dimensional space coordinates in the robot coordinate system;(3)The kinematics analysis of the robot was carried out.Based on the D-H parameter method,the kinematics model of the robot was established,the direct and inverse kinematics equations of the robot were deduced,and the correctness of the equations was verified;(4)The robot trajectory planning method and its evaluation criteria were proposed.The polynomial interpolation method and cubic spline function interpolation method were used to plan the trajectory of data points in the joint space,and the simulation experiments were carried out to put forward the evaluation standard of robot trajectory planning.The root mean square formula was used to compare several interpolation methods,and it is concluded that the speed of the robot was the highest with the cubic spline function interpolation method,which met the research needs;(5)Optimization of the time-optimal trajectory of the robot was completed.A time-optimal trajectory optimization model was established,which imposed kinematic constraints on each joint,based on the five-order B-spline trajectory planning,and used the improved particle swarm hybrid algorithm to optimize the time-optimal trajectory of the grinding trajectory;The innovations of this thesis are as follows.(1)An automatic generation method of data points of the grinding path was proposed.The data points of the grinding path were automatically obtained by machine vision and digital image processing technology;(2)The evaluation criteria of robot trajectory planning method were proposed.The root mean square was used to compare the two polynomial interpolation methods with the cubic spline function interpolation method.Under the same working conditions,the interpolation method with higher speed was selected can improve the grinding efficiency;(3)An improved method of particle swarm optimization was proposed.Basd on the particle swarm algorithm with random weights,combined with the natural selection algorithm.The time-optimal trajectory optimization model was established,and the improved particle swarm algorithm was used to optimize the grinding trajectory time of the parts to obtain the optimal trajectory.