Research On Path Planning Of Intelligent Vehicle Based On Improved A* Algorithm And Artificial Potential Field

Intelligent vehicle is widely used in all aspects of production and life.In order for a intelligent vehicle to successfully complete a given task,it is necessary to let it arrive at the designated place autonomously.Therefore,it is of great value to study the path planning technology of intelligent vehicle.However,most path planning studies only stay in the theoretical stage of algorithm research,and these algorithms have certain limitations and shortcomings to varying degrees.For example,the path of A* algorithm planning is long,the turning angle is too large,and it is not smooth enough,and the artificial potential field method has target unreachable and local minimum value defects.At the same time,it is also a difficult problem to use hardware to implement path planning for mobile robots.In this paper,the shortcomings A* algorithm and artificial potential field method are improved and optimized,and the two are combined to design a hybrid path planning algorithm.Finally,the actual application of path planning on the rikirobot intelligent vehicle hardware platform has a good effect.The main research work and results are as follows:1.Aiming at the shortcomings of A* algorithm in global path planning,the path is too long,the turning angle is too large,and the path is not smooth enough.An improved algorithm for iterative optimization of path in A* algorithm by intelligent ant colony algorithm is proposed.Through the comparative simulation verification under matlab,it can be seen that the improved A* algorithm has a shorter path,less total turning angle and a smoother planned path when planning the path.2.The disadvantages of target unreachable and local minimum values for local path planning based on artificial potential field method are improved by using the modified potential field method and introducing escape force.The simulation shows that the improved artificial potential field method overcomes the local minimum and target unreachability problems.3.Using A* algorithm in the global path planning,the search path is faster,the algorithm is simple,and the artificial potential field method has the advantages of high real-time and partial obstacle avoidance effect in local path planning.and the improved A* algorithm is combined with the artificial potential field method to design a hybrid path planning algorithm.The simulation verifies the hybrid path planning algorithm.4.The proposed hybrid path planning algorithm is applied in real environment.Firstly,build the Rikirobot intelligent vehicle hardware platform and analyze the its model,and configure the ROS kinetic and the host computer experimental environment.After the communication connection is established between the upper computer and intelligent vehicle,the navigation parameter configuration,the angular velocity linear velocity correction,the IMU correction,and the PID dynamic correction are first performed on the smart car.Then,the intelligent vehicle is remotely controlled by the host computer,and the surrounding environment information is collected by the 2D laser radar carried by the vehicle,and the environment map is constructed by using the SLAM algorithm package provided in the ROS.After that,the car plans an optimal path according to the hybrid path planning algorithm,avoiding obstacles and successfully reaching the target point.The success of the experiment shows that the hybrid path planning algorithm is not only theoretically effective,but also practical in practice.