Path planning of mobile robot based on improved genetic algorithm

Abstract

In order to solve the problems of slow convergence speed and avoid local optimum in the path planning of mobile robot, the basic genetic algorithm was improved and a method for path planning of mobile robot in static environment was proposed. In this paper, the shortest planning path and the adaptive smoothness are combined as the influencing factors of the individual fitness function value of the path, and a certain weight is assigned to these two factors. It improves the local optimal solution of the basic genetic algorithm, overcomes the shortcoming of precocity, and improves the global search ability of the algorithm. The simulation results show that the improved genetic algorithm is feasible and effective in the path planning of mobile robots.