Research On Real-time Obstacle Avoidance Of Mowing Robot Based On IAPF And MPC

In the field of intelligent robot,real-time obstacle avoidance is one of the core technologies to realize high-end intelligent robot.The real-time obstacle avoidance of mowing robots mainly focuses on local path planning and path tracking.Artificial Potential Field(APF)is adopted for path planning,and Model Predictive Control(MPC)is combined for path tracking control.The research of path planning and tracking technology plays a crucial role in the development and maturity of the field of robotics.In order to realize the mowing robot can efficient real-time obstacle avoidance of obstacles environment or unknown environments,the purpose of the design and build the four-wheel independent drive mowing robot platform,the environment around the robot through the environment perception sensor for sensing,GPS/IMU navigation and positioning for robot pose information itself,with the Improved Artificial Potential Field method(Improved Artificial Potential Field,IAPF)for path planning and path following MPC algorithm,combined with the advantages of each other,mowing robot can quickly and accurately generate obstacle avoidance path and track the path.This paper first explains the basic theory of path planning control,aiming at the disadvantage of the APF exists and the insufficiency to improve,respectively,to improve the attractive potential field and the repulsion potential field,and deduce IAPF model is established,and path planning evaluation model is established to evaluate quality of path planning,path planning and designing the controller and compared with other methods for test.The results show that the designed method can not only solve the problems that the robot cannot reach the target near the obstacle,the robot is easy to oscillate near the obstacle and the robot is easy to fall into the local minimum.According to the evaluation model of path planning,the method is obtained to improve the quality of the whole path planning,improve the operating efficiency of the robot,and make the improved algorithm more practical and efficient.Then,a path tracking controller based on MPC algorithm is established for the real-time obstacle avoidance task of mowing robot.Starting from the basic theory of the MPC algorithm,using linear time-varying model predictive control based on the actual demand,will cut the grass as the path tracking controller of robot kinematics model prediction model,and then design the objective function and constraint conditions and to solve optimization problem,and finally,set up three typical reference path for the path tracking controller based on MPC simulation analysis.The experimental results show that the designed controller can quickly eliminate the error and finally reach the stable state even if the robot has some initial error,and can accurately track the reference path.Finally,the robot and controller are simulated and verified and analyzed.Based on the platform of the constructed mowing robot,the simulation and real vehicle experimental design are carried out.The simulation results show that the controller designed based on IAPF and MPC can control the path of the robot tracking planning smoothly,and the maximum error between the tracking path and the reference path is no more than 7cm.The vehicle experiment shows that the maximum error between the tracking path and the reference path is always within ±21cm,but the deviation is within the allowable range of the controller error,which indicates that the mowing robot can effectively track the reference path and meet the control requirements of the robot.This proves that the mowing robot can accurately and efficiently achieve real-time obstacle avoidance and complete the assigned tasks,and also proves the feasibility and effectiveness of the designed controller in the real vehicle environment.