Robot Path Planning Method Based on Genetic Algorithm

Abstract

Intelligent mobile robots are robots that can automatically complete driving tasks without human intervention. Route planning is the main problem in robotics research. The goal is to find the lowest cost, conflict-free route from the known starting point of the obstacle route to the known end point. Genetic algorithms are based on the natural selection of populations and the random, repetitive and evolutionary processes of genetics. This is a very effective algorithm in the field of route planning research. In this article, I will first explain the design principles of mobile robots. After comparing the advantages and disadvantages of various routing methods, a genetic algorithm was chosen to solve the routing problem of mobile robots. Then, through detailed research on genetic algorithm, a solution to robot path programming based on genetic algorithm in static and dynamic environments is proposed. In the design of genetic operons, standardization, insertion and deletion of operons have been added to make up for the lack of basic operons. At the same time, the genetic algorithm is optimized through adaptive mutation and crossover rate. You can improve the algorithm by adding new operators and adaptive methods to solve the problem that the target point cannot be reached due to the local minima in the evolution process. Finally, in three static environments with different levels of complexity, simulation experiments are carried out and the simulation results are analyzed. The influence of different fitness parameters on the path planning results is discussed, and the dynamic path planning simulation is carried out. Through comparison with other methods, it can be found that in the same environment, the path planning method based on genetic algorithm saves at least 5% time than Dijkstra algorithm in search time.