Path Planning of Mobile Robot Based on Genetic Algorithm with Predictive Operator and Dynamic Parameters

The standard genetic algorithm has achieved great success in solving various optimization problems, but in practical applications, owing to the lack of the use of specific knowledge, the results are not satisfactory. Aiming at the practical application of robot path planning, an improved genetic algorithm applied to robot path planning is proposed by adding a prediction mechanism method to improve the chromosome coding method and fitness function in standard genetic algorithm. In the process of introducing map feature information into the initial population, the evolutionary efficiency of the algorithm is improved. And the dynamic parameter setting strategy is adopted, which makes the algorithm have the ability to escape the local minimum and improve the convergence of the algorithm. The numerical simulation results show that the improved algorithm has a significant improvement in the convergence value and the convergence rate.