Study On The Genetic Algorithm Of Mobile Robots'Fast Path Planning

With the development of robots’industrial,how to improve intelligent mobile robots’fast path planning has been the hot field of robotics. Intelligent mobile robot path planning is divided into global path planning and local path planning,each part has different mathematical modeling and algorithms adapted to the optimal path.This article looks at the global path planning, using the grid method to describe environment, based on the genetic algorithm study of robot’s path-planning, given a suitable robot-simulation of chosing the optimal path. This robot-simulation can select obstacle objects,starting point and target artificially, display the path based on improved genetic algorithm in simulation block, shown real tracing by NWU-RR-I intelligent mobile robot. A new model of fitness function is proposed which reduce the opportunities of invalid grid point involved in path planning,improve the safety of path and less the algorithm’s time. Introduces the idea of the parent node to the delete operator,improved the probability of redundant paths’ deletion.Making the step as algorithm constraints will be improve the robot’s probability of exit.During experimental validation,using VC++6.0software simulation for robot path planning’s before and after improvement genetic algorithms,analysising different conditions of experiment results proved the improved algorithm’s superiority,then the NWU-RR-I intelligent mobile robot for improved algorithm to verification of the algorithm is reasonable and correct.