Research And Application Of Mobile Robot Path Planning Based On Artificial Intelligence

Mobile robot path planning is an important research direction of mobile robot. The mobile robot path planning is an important guarantee for mobile robot moving fast, accurately, smoothly. In actual environmental the path where mobile robot from the given initial position moves to the given target position is multiple and complex. In order to realize that mobile robot can quickly accord the optimal path to move from the given initial position to the given target position safety and without touch, this paper adopt ant colony algorithm as path planning method. The theoretical of path planning algorithm provides feasible program for mobile robot moving from the given initial position to the given target position fast and safely.This paper starts from the basic theory of ant colony algorithm and uses the ant colony algorithm to solve traveling salesman (TSP) problem to analyze the step of ant colony algorithm and parameters setting problem. Use the MATLAB for simulation. The influence of parameter of ant colony algorithm is one by one analysis for the better realize the path planning of mobile robot. In view of the realization of ant colony algorithm in TSP take ant colony algorithm as mobile robot path planning. Use the MATLAB for simulation, Simulation results show that the ant colony algorithm can be very good at getting the optimal path for mobile robot.According to the basic parameters of ant colony algorithm on its influence and the essential parameter of the algorithm set by experience set without theory. This paper adopt multi parameter concatenated encoded genetic algorithm as optimize the parameter of ant colony algorithm method, which is not only to optimize the parameters of ant colony algorithm, but also to provide the theoretical basis. Use MATLAB for simulation. Simulation results show that the genetic algorithm optimize the parameter of ant colony algorithm which can achieve a better combination of parameters, allowing the path better.Finally, in order to verify that the ant colony algorithm obtained conclusions can be applied to mobile robot path planning problem in the actual environment, use the Shanghai xPartner Robotic’ INNOX IN-R robot as a verification platform. Results of the algorithm are transformed to robot motion geometric parameters, real-time control implementation in the actual environment, path planning and motion combined with structure characteristics of robot. Application of ant colony algorithm for mobile robot path planning, which explain ant colony algorithm has higher reliability and combining with other algorithm, and verify the algorithm for the mobile robot in the actual environment, path planning to provide more rapid path optimization scheme, and has the value of practical application.