Intelligent Path Planning of Mobile Robot Based on Genetic Algorithm

Abstract

Abstract In this study, in order to deal with the path planning problems with genetic algorithm (GA) and improve its time efficiency, a new method combined with K-Nearest Neighbor (KNN) is proposed. GA is a well-known approach to figure out a path between two designated points while avoiding obstacles in the way. In traditional GA process, a set of solutions from the starting node to the destination are randomly generated in the beginning, which are called initial population. Although not every solution is feasible and the feasible ones may not be ideal, optimal path is likely to be obtained during the iterations. A new method is designed to reduce the number of iterations by constructing a dataset using traditional GA and selecting elite solutions as initial population when facing a new map. The selection criteria is the similarity between the distribution of obstacles and previous ones. Relevant experiments are organized to test the performance of the proposed method by comparing it with the traditional GA. According to the simulation results, the proposed method with elite initial population tends to converge more quickly.