Improved Genetic Algorithm for Solving Robot Path Planning Based on Grid Maps

Aiming at some shortcomings of the genetic algorithm to solve the path planning in a global static environment, such as a low efficiency of population initialization, slow convergence speed, and easy-to-fall-into the local optimum, an improved genetic algorithm is proposed to solve the path planning problem. Firstly, the environment model is established by using the grid method; secondly, in order to overcome the difficulty of a low efficiency of population initialization, a population initialization method with directional guidance is proposed; finally, in order to balance the global and local optimization searching and to speed up the solution speed, the proposed non-common point crossover operator, range mutation operator, and simplification operator are used in combination with the one-point crossover operator and one-point mutation operator in the traditional genetic algorithm to obtain an improved genetic algorithm. In the simulation experiment, Experiment 1 verifies the effectiveness of the population initialization method proposed in this paper. The success rates in Map 1, Map 2, Map 3, and Map 4 were 56.3854%, 55.851%, 34.1%, and 24.1514%, respectively, which were higher than the two initialization methods compared. Experiment 2 verifies the effectiveness of the genetic algorithm (IGA) improved in this paper for path planning. In four maps, the path planning is compared with the five algorithms and the shortest distance is achieved in all of them. The two experiments show that the improved genetic algorithm in this paper has advantages in path planning.