Research On Vision-based Motion Planning And Control Of Spraying Robots

In recent years,with the rapid development of machine vision,artificial intelligence and other technologies,the application of robots in the field of industrial spraying is becoming more and more widespread.However,whether based on manual dragging and teaching or based on offline programming,spraying robots have problems such as cumbersome workflow,low operational efficiency and poor spraying quality when facing multiple non-planar workpieces to be sprayed.Based on the above problems,this thesis applies machine vision technology to the spraying robot system based on the comparative analysis of existing spraying robot trajectory planning and motion control methods.The surface images of the workpiece to be sprayed are collected by vision sensors,and the corresponding data processing and spraying path planning is carried out according to the shape of the workpiece,in order to simplify the process of spraying trajectory planning and improve the degree of automation.In addition,the effectiveness of the spraying robot in tracking the spraying trajectory is investigated by analysing the mechanism and parameters of the spraying robot,establishing its kinematics and dynamics model,and proposing a composite control strategy based on model predictive control algorithms and fuzzy logic theory to achieve efficient and highly accurate tracking control of the spraying trajectory.The main work of this thesis is as follows:First,a vision-based spraying robot system is built,which consists of two parts: the vision system and the robot system.By comparing and analysing different vision acquisition solutions,the Kinect depth camera,which can acquire the depth map of the workpiece,is selected as the vision acquisition device,and the camera imaging model,camera calibration method and hand-eye calibration method are analysed and studied.Secondly,the calibration experiments of the Kinect colour camera and the depth camera were completed with a home-made calibration board to realise the alignment of the colour camera and the depth camera,and to complete the hand-eye calibration experiments of the robot and the camera.The Kinect depth camera is used to collect the depth map of the workpiece to be sprayed,and the depth image information is converted into a 3D point cloud,which is filtered,streamlined and visualised through point cloud data processing,and the point cloud normal vector is extracted and unified in orientation to provide data support for subsequent spraying trajectory planning.Then,the spraying path is selected according to the shape of the workpiece to be sprayed and the distribution of the point cloud;the 3D point cloud of the surface of the workpiece to be sprayed is mapped to the joint space by modelling and analysing the forward and reverse kinematics of the spraying robot,and the trajectory planning of the spraying robot is completed in the joint space and the spraying trajectory is visualised.Finally,the dynamics of the spraying robot is analysed and modelled,its motion is optimally controlled,and a predictive control strategy for tracking the spraying trajectory of the robot arm incorporating fuzzy compensation is proposed.The planned joint motion trajectory is used as the target trajectory,and the results are simulated jointl by MATLAB and SimMechanics,and the proposed control strategy improves the tracking control accuracy compared with the classical model predictive control algorithm.