An improved genetic algorithm for mobile robot path planning in grid environment

Abstract

This paper proposes an improved genetic algorithm to study the path planning of mobile robot in grid environment. First, rasterize the motion plane of the robot, use serial number coding method and design a heuristic median insertion method to establish the initial population, ensure that the planned initial paths are all feasible paths, thereby speeding up the convergence of the algorithm. Then assign different weights to the path length, path security, and path energy consumption and combine them to generate a multi-objective fitness function. Finally, improve some genetic operations to maintain the population diversity of the algorithm in the later period, and avoid the algorithm from falling into avoid premature. Simulation experiments show that the proposed algorithm can quickly plan a feasible path in the grid environment. The path is not only shorter in length, but also more stable. At the same time, the running speed of the algorithm is 45.7% higher than other improved algorithms.