Research on path planning of mobile robot based on improved genetic algorithm

A modified genetic algorithm was proposed to address the issues of slow population evolution，and longer optimal path obtained by traditional genetic algorithms when solving path planning problems under the sampling point model．To enhance the quality of the initial population，adaptive adjustment of step size was suggested to limit the selection range of offspring nodes，within which random selection was conducted．Two parent paths were randomly selected，forming a rectangular search area for offspring nodes between corresponding pairs of nodes．A point was chosen from each area，and connections were made sequentially to obtain offspring individuals after crossover，thereby avoiding ineffective crossovers due to insufficient intersection points in the sampling point model．To address the issue of unpredictable mutation effects，the line connecting the preceding and succeeding points of the selected mutation parent node served as a guide．The closer a node was to this line，the higher the probability of it being selected as the mutation offspring node，rendering the mutation point selection more directional．Comparative experiment results show that the proposed modified genetic algorithm effectively improves pathfinding efficiency when dealing with path planning problems based on sampling points．The convergence speed of the optimal path using the modified algorithm is approximately 60% higher than that of traditional genetic algorithms，and the length of the optimal path is reduced by up to 2.42 m，with the highest improvement in the convergence speed of the optimal path reaching 56% compared to other literature algorithms. © 2024 Huazhong University of Science and Technology. All rights reserved.