Mobile robot path planning method based on improved genetic algorithm

Abstract

The invention discloses a mobile robot path planning method based on an improved genetic algorithm. The mobile robot path planning method comprises the following steps of carrying out environment modeling on a robot moving space by using a grid map, setting algorithm parameters and initializing a population, constructing a fitness function by utilizing the path length function and the smoothness function, introducing an elitist retention strategy, namely, when roulette selection is carried out, retaining the optimal individual to the next generation, and continuously carrying out crossover mutation operation, dynamically adjusting the population by adopting an adaptive crossover rate and a mutation rate, and judging whether the evolutionary frequency reaches the maximum or not, if so, outputting an optimal solution, and if not, repeating the steps. When the method is applied to path planning of the mobile robot, the optimal solution searching capacity of the mobile robot is enhanced, the convergence speed of the mobile robot is increased, and the turning frequency is reduced.

Description

technical field [0001] The invention relates to the field of robots, in particular to a path planning method for a mobile robot based on an improved genetic algorithm. Background technique [0002] Mobile robots can be designed and planned by humans to replace humans in some dangerous and complicated tasks. Therefore, the path planning of mobile robots is particularly important in robotics. In recent years, scholars at home and abroad have proposed a variety of algorithms to solve the problem of mobile robot path planning, including genetic algorithm, particle swarm algorithm, artificial potential field method, ant colony algorithm, etc. Among them, the particle swarm algorithm can achieve rapid convergence but may be stagnant; the artificial potential field method is relatively simple in calculation, but may not reach the target point; the ant colony algorithm has strong robustness but is easy to fall into a local optimal solution. Among them, the genetic algorithm has th...

AI Technical Summary

Problems solved by technology

Among them, the particle swarm algorithm can achieve rapid convergence but may be stagnant; the artificial potential field method is relatively simple in calculation, but may not reach the target point; the ant colony algorithm has strong robustness but is easy to fall into a local optimal solution

Among them, the genetic algorithm has the ability of global search, but its disadvantages cannot be ignored, such as falling into local optimum, slow convergence speed, and poor optimization stability.

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