Research On Motion Trajectory Planning And Control Technology Of Vision-guided Industrial Robot

Currently,as the level of industrial automation is getting higher and higher,the use rate of robots is getting higher and higher.Many scholars at home and abroad have done a lot of research on visual guidance technology and trajectory planning technology.many academics have done a lot of research on visual guidance technology and trajectory planning technology,but the application of visual guidance technology on the planning of rectangular coordinate robots is still relatively rare.As an important branch of robots,Cartesian coordinate robots are widely used because of their high accuracy and high reliability.The Cartesian coordinate robot studied in this paper will be applied to a grinding wheel dicing machine.In view of the fact that the accuracy of visual detection cannot meet the accuracy of existing servo systems,an edge detection algorithm is proposed that aim to improve the accuracy of trajectory recognition,and then improve the accuracy of trajectory planning.The main research contents of this article are as follows:Firstly,according to the practical application of the Cartesian robot,the overall mechanical structure of the Cartesian robot is designed as follows: a four-degree-of-freedom robot including X,Y,Z and a rotating platform,and the kinematics and dynamics analysis of the designed robot.The second is the hardware selection of the robot control system,while this paper analyzes the advantages of traditional PID algorithm and fuzzy control algorithm.In connection with the motion characteristics of Cartesian robot,a self-tuning fuzzy PID control algorithm combining PID algorithm and fuzzy control algorithm is cited as the robot motion control algorithm.The core of the algorithm is to use a computer to imitate human thinking,adjust the three parameters of PID in real time,use natural language instead of quantitative terms to approach human decisions,and use MATLAB to simulate experiments.The next step is to select the hardware for the robot's vision system,including cameras and light sources.Aiming at the problem that the accuracy of visual inspection cannot meet the accuracy of existing servo motors,aiming at the problem of low accuracy of edge recognition,this paper proposes an improved trajectory edge recognition algorithm based on traditional Canny edge extraction algorithm,which combines bilinear interpolation and sub-pixel edge detection algorithm.The algorithm realizes high-precision track edge recognition,and the edge recognition simulation of chip cutting track by Halcon image processing software proves the feasibility of the algorithm.Finally,build an experimental platform to complete the visual guided trajectory accuracy experiment through the built experimental platform.The error between the ideal trajectory and the actual trajectory is less than 0.001 mm.The experiment proves that the improved edge detection algorithm accurately locates the position of the trajectory under the premise of accurately identifying the edge of the trajectory,thereby improving the accuracy of trajectory planning.