Research On Path Tracking Of Four-wheel Steering AGV Based On Ahrs And Magnetic Navigation Sensor Data Fusion

The fruit picking robot is an intelligent agricultural equipment that can independently complete fruit positioning,picking,transportation and other tasks.As the carrier of the picking robot,the stability of the autonomous navigational walking system directly affects the entire fruit picking process.Therefore,how to obtain the posture of the robot by the self-carried sensor in the working environment and realize the automatic navigation operation has very important scientific significance for the accurate fruit picking.Based on the analysis of the research progress of the domestic and foreign agricultural robots walking system and navigation and positioning methods,this paper analyzed the kinematics of an autonomously designed four-wheel steering AGV according to the requirements of the picking operation of agricultural robots.The AGV positioning method of multi-sensor information fusion and the research on the path tracking control of magnetic navigation based on the fuzzy controller were carried out to realize the autonomous navigation path tracking of the fruit picking robot.The main research contents of this article are as follows:Firstly,aiming at autonomously designed four-wheel steering AGV,the operating environment such as greenhouses and orchards was considered to meet the requirements of high environmental adaptability,mobility,and smooth navigation path to achieve fruit picking operations.The kinematic model of four-wheel steering AGV various operating modes and the global coordinate pose positioning was derived,which provides the mathematical foundation for AGV pose positioning,automatic navigation and multi-function motion control.Secondly,the model of multi-sensor information fusion system and the advantages and disadvantages of commonly used data fusion methods were analyzed.And the detection methods and principles of attitude and heading reference system(AHRS),magnetic navigation sensor,rear wheel AC servo motor encoder were introduced for the autonomously designed four-wheel steering AGV.Unscented Kalman filter(UKF)combined with AGV pose positioning model was used to analyze,correlate and fuse the information of multiple sensors to obtain the optimal target estimation of AGV pose.Then,in order to realize the magnetic guidance path tracking of the four wheel steering AGV,the method of detecting the guidance path deviation of the magnetic navigation sensor was studied.According to the experience of human driving,the trajectory tracking fuzzy controller was designed by use the position deviation and angle deviation of AGV with navigation path as input and the vehicle steering angle and speed as output.In the Smulink environment of MATLAB,different guidance paths were designed.And the AGV path tracking dynamic simulation was carried out in different pose states to verify the rationality of the fuzzy controller design and the correctness of the model.Finally,according to the control requirements,the AGV underlying motion control program based on the PLC controller and the sensor information acquisition,data fusion algorithm and path tracking program based on Labview platform were design.AGV pose position fusion experiment was carried out by the autonomously designed four-wheel steering AGV platform.The location fusion experiment shows that the AGV fusion pose error is less than 50 mm,and the heading error is less than 4°,indicating that the UKF filter data fusion method can effectively improve the position and orientation accuracy of the AGV and verify the correctness of the data fusion algorithm.Meanwhile,the snake shape trajectory and the S-bend trajectory was designed to carry out AGV path-tracking test.The test showed that the fuzzy controller can adjust the AGV speed and the guide angle of the AGV in real time.The position deviation during navigation is less than 53 mm.At the same time,the controller can ensure the AGV operation efficiency under the requirements of the control accuracy during the running process and verify the effectiveness of the designed path tracking fuzzy controller.