Research On Path Planning Of Wheeled Mobile Robot

Nowadays wheeled mobile robot is applied to various important occasions with its excellent flexibility and mobility.In the field of mobile robots,path planning is an indispensable part of the robot navigation technology.It's not only the cornerstone of the mobile robot to complete the high-level task,but also an important symbol of the robot's realization of the intellectualization of the robot.Efficient path planning is the key to achieving the established task.In this paper,a wheeled mobile robot in static environment is taken as an example to study the path planning based on the grid method and ant colony algorithm.Firstly,the grid method is used to model the environment.The environmental information is mapped to the grid map and the identification method of the grid is determined.Then some improvements are made to the existing grid method to avoid the mobile robot falling into the deadlock.The basic ant colony algorithm is used to study the path planning on the improved grid map.The mechanism and principle of the basic ant colony algorithm are expounded in the deep meaning and some systematic features of the basic ant colony algorithm are analyzed.Then the influence of the important parameters of the basic ant colony algorithm on the optimization of the algorithm is simulated.The configuration range of the important parameters is selected through a large number of experimental data.Secondly,the performance of the algorithm is optimized by improving the updating mode of pheromone volatilization coefficient and using particle swarm optimization to optimize the important parameter of ant colony algorithm.Because the path planning of the ant colony algorithm is not smooth enough,the Bessel curve is used to optimize the path of smooth planning,which can improve the flexibility of the robot motion.Finally,the kinematics modeling of the mobile robot based on the Macanum wheel is built and the path following simulation model is built in MATLAB with Simulink and fuzzy toolbox.The experimental results show that the mobile robot can efficiently plan an optimal path and complete the path tracking task in the static and complex environment.