Research On Smart Car Positioning And Path Planning Based On Ultra-wideband

The smart car integrates functions such as environmental awareness,control decisions,and autonomous movement.With the development of science and technology and people's increasing demand for smart vehicles,research on smart cars has also attracted the attention of more and more researchers.In many cases,smart cars need to be used in indoor environments.Positioning and path planning are important research contents of smart cars.The positioning function provides smart cars with location information in a sports environment.Path planning provides a safe collision-free path from the starting point to the target point for the smart car,which is a prerequisite for the safe movement of the smart car.Therefore,this thesis studies the indoor high precision positioning technology and intelligent path planning technology of smart cars.The specific research contents and research results are as follows:(1)UWB technology and wireless location methods are introduced in detail.By analyzing and comparing the advantages and disadvantages of two types of wireless location methods based on ranging and no ranging,it is determined that a location method based on ranging is used for location ranging in this thesis.The simulation analysis compares the performance of commonly used wireless location algorithms.Based on the simulation analysis results and the characteristics of each algorithm,the trilateration algorithm is used to locate the smart car.Aiming at the problem of low precision of single positioning algorithm,this thesis studies a trilateration measurement hybrid algorithm based on Kalman filter and Taylor algorithm.The simulation results show that the hybrid algorithm is superior to the single trilateration algorithm in positioning accuracy.The hybrid algorithm can provide better positioning results for smart cars.(2)Based on the ultra-wideband positioning technology,the path planning method of smart car was studied.For the disadvantage that the artificial potential field method is easy to fall into the local minimum point,this thesis adopts a path planning method based on the combination of the artificial potential field method and the wall-following method based on the UWB positioning technology,and determines the switching conditions of the artificial potential field method and the wall-around method.Simulation results show that the algorithm can not only solve the shortcomings of the artificial potential field method that can easily fall into local minimum points,but also give the switching conditions of the artificial potential field method and the wall-following method in time,avoiding the path redundancy problem of the wall-following method.For the target unreachability problem,this thesis adopts an improved method of repulsive field function,which adds the distance from the moving object to the target point to the repulsive force function.The simulation results verify the effectiveness of the improved method.(3)Aiming at the ever-changing information of using environment maps,this thesis studies an artificial potential field method dynamic path planning algorithm based on grid method environment modeling.This method can dynamically update maps and paths based on the actual detected environmental information.The simulation results verify the feasibility of the algorithm.(4)The hardware platform of the smart car was introduced,and the software control system of the smart car was designed.Software control system includes upper computer software interface and lower computer control program.Based on the smart car mobile platform,the positioning accuracy of the car under static and moving conditions was tested.Experimental results show that the hybrid algorithm improves the positioning accuracy of the smart car.