Research on Robot Path Planning Based on Improved Genetic Algorithm

In order to solve the problems of slow convergence speed and easy to fall into local optimum in solving the robot path planning problem, this paper improves the basic genetic algorithm. This paper introduces the artificial potential field method to initialize the population, and proposes an adaptive selection method based on the evaluation of the degree of population diversity. The adaptive crossover probability and mutation probability are designed to improve the algorithm solution quality, and multiple simulations are carried out in the grid environment to further prove the feasibility and effectiveness of the algorithm.