Research On Dynamic Path Planning And Tracking Optimization Of Unmanned Vehicle Based On Model Predictive Control

The occurrence of road traffic accidents has brought about huge losses to people's lives and property safety.Therefore,it is of great significance to seek a method to reduce the incidence of traffic accidents.Due to its intelligent features,unmanned driving technology makes it possible to alleviate traffic safety problems and reduce the incidence of traffic accidents.The basis and key of achieving unmanned driving is the path planning and trajectory tracking of unmanned vehicles.However,the limitations of the planning and tracking method itself,the complexity of the unmanned vehicle driving environment and the dynamic uncertainty have greatly hindered the optimal feasible path and real-time tracking trajectory of the unmanned vehicle.Although the research on unmanned vehicle path planning and trajectory tracking based on Model Predictive Control(MPC)has made some progress in recent years,there is still much room for improvement in the optimal path and tracking accuracy and real-time performance.Therefore,it is necessary to further study the dynamic path planning and trajectory tracking of unmanned vehicles based on model predictive control.The main research contents of this thesis are as follows:(1)Modeling of unmanned vehicle driving environment based on improved Artificial Potential Field(APF).First,based on the factors affecting the driving of the existing unmanned vehicles(obstacles,roads,other environmental vehicles and target points),the differences between the same types of factors are distinguished: the obstacles are divided into obstacles that can be crossed and cannot be crossed;the road boundaries and lane lines are also considered separately.Secondly,by analyzing the characteristics of each influencing factor,the potential field function is redesigned separately.Secondly,the appropriate weights are assigned to the influencing factors in the APF to represent the degree of various factors affecting vehicle travel.Then by introducing temporary obstacles,it overcomes the shortcomings of traditional APFs that are easy to fall into local optimum.Finally,the traditional APF and the improved APF are compared in MATLAB to verify that the improved APF has better path planning and obstacle avoidance performance,and will not fall into local optimum.(2)Improvement and optimization of path planning and tracking algorithm based on model predictive control.The environmental potential field model established in(1)is added as a path planning item to the objective function of the MPC,so that path planning and trajectory tracking are simultaneously performed.Secondly,for the case that the MPC objective function may have no optimal solution,the adjustment factor of the objective function is introduced to form a new MPC objective function.Then the objective function is linearized to accelerate the solution process of the objective function.Finally,using MATLAB and CarSim co-simulation to carry out experiments and design single car driving scenes(follow-up,lane change,obstacle avoidance,straight road and corner),it is verified that the improved and optimized MPC planning path and tracking trajectory are more accurate and the solving process is accelarated.(3)Design of driving control for unmanned vehicles based on APF and MPC.On the basis of(2)driving control of one single car is expandedto motorcade in groups.First,refer to the modeling method in(1)for environmental modeling and build a team driving environment model.Secondly,designing strategy of unmanned vehicle team driving,and then the vehicle team driving controller is designed.The unmanned vehicle motorcade driving controller is designed according to the established environmental model.When designing the controller,the target function of the guided vehicle and the following vehicle are separately designed: the guiding vehicle adopts the same controller as the controller in(2),and the following controller adds the reference trajectory item on the basis of the guiding vehicle controller.Finally,a variety of experimental scenarios(obstacle avoidance and on straight and corners,overtaking,etc)are designed.Simulation experiments are carried out on the joint simulation platform of MATLAB/CarSim to test the control effect of the controller designed in this thesis.In this study,the improvement of the artificial potential field and the improvement of the environmental potential field provide the basis for the MPC to plan a more accurate path.Then,by optimizing the MPC,the solution speed and trajectory tracking accuracy are improved.Finally,On the basis of driving control of one single car is expandedto motorcade in groups.Designed a team controller to realize the vehicle team driving.This research has certain theoretical significance and practical value for the path planning and trajectory tracking of unmanned vehicles and vehicle group driving.