Research On Path Tracking Control Of Heavy-duty AGV Based On Multi-source Positionin

Under the background of China’s opening to the outside world,the container throughput of port terminals is increasing day by day and accompanied by the technological trend of unmanned driving technology,the research on heavy-duty AGV vehicles has become one of the current hot spots.Therefore,this paper carries out the following research work on the path tracking control problem of heavy-duty AGV vehicles.Firstly,the vehicle dynamics model of heavy load AGV is built.In Matlab/Simulink,the 11-d OF dynamics model of the vehicle was established.The steering wheel angle and the target speed were taken as the input of the dynamics model,and the driving state information was determined by the output parameters such as lateral speed and longitudinal speed.Finally,Trucksim software and dynamics model were used to compare the step angle input and sinusoidal angle input of steering wheel,and the accuracy of dynamics model was verified according to transverse acceleration curve and yaw velocity curve.Secondly,under the condition of GPS positioning information,aiming at the influence of the axle length of heavy AGV vehicle on steering,a method of coupling the axle length and reference path was creatively proposed.The global deviation model between axle and reference path was established and the optimal heading angle was determined by solving the model.The model predictive controller was designed based on the optimal heading angle and reference path,and the cost function including the path tracking error and control input was established,and the optimal steering angle was determined by solving the cost function.Finally,it is verified that the optimal heading angle can effectively reduce the vehicle body sweep area during the steering process and improve the trafficability of heavy AGV vehicles under various conditionsFinally,QR code images are combined with camera and odometer to assist positioning research.In view of the impact of QR code spacing on visual positioning,innovative proposed to establish the track error and distance length model,combined with the size of the QR code image to determine the maximum QR code spacing distance.Finally,an extended Kalman filter algorithm is designed to fuse odometer and visual positioning data.The simulation results show that the data fusion algorithm can improve the precision of the integrated positioning system.