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

Abstract

The basic genetic algorithm usually has the following problems when solving robot path planning problems. First, the chromosome (path) of the first generation is randomly generated, so its fitness is often too high, resulting in too slow convergence speed and even unable to obtain the global optimal solution. Second, in the crossover operator, the selection of crossover points is often random, which cannot ensure that the fitness of cross generated offspring is better than that of their parents. Finally, in the mutation stage, because the traditional mutation strategy is to replace the selected mutation node in the individual chromosome with a random node, the offspring path generated by the mutation operator is likely to be discontinuous, so it cannot reflect the effectiveness of the mutation operator. Based on the mentioned contents, some improved strategies are proposed for the discussed three shortcomings of the basic genetic algorithm. First, a new generation mechanism of the first generation is constructed by simulating the path finding rules of ants in ant colony optimization, which greatly improves the quality of the first generation. Second, during crossover operator, the chromosomes of the two parents are disconnected at the same node, and the optimal chromosome fragments in each segment are combined based on greed, so as to gather all the excellent gene fragments of the parents in an individual as much as possible, improve the quality of the offspring after crossover operator, and speed up the convergence speed. Finally, a new mutation strategy is proposed to eliminate redundant sections and the number of corners, so that the individuals always can be mutated in a good direction. A large number of simulation results show that the improved genetic algorithm is effective in solving the robot path planning problem, and the overall performance is better than the basic genetic algorithm and some other improved genetic algorithms. Finally, by modeling and simulating the real enterprise factory environment based on the ROS platform, the experimental results also verify the effectiveness and feasibility of the improved genetic algorithm.