Bidirectional path planning based on improved genetic algorithm
Jie Xu, Likai Xu · 2nd International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2022) · 2022
Aiming at the problems of premature convergence and long time-consuming in traditional path planning of mobile robots, a bidirectional path planning method based on an improved genetic algorithm was designed. Firstly, an environment map is established, and the physical space is abstracted into an abstract space that can be processed by the path planning algorithm by using the grid method. Then, simulated annealing algorithm is used to improve the population selectivity and diversity; and indicators such as path tortuosity are added to the fitness function, and two-way search rules are added and set. Improve the smoothness of the path of the mobile robot, making it easier to drive. And using matlab for simulation verification, the results show that compared with the classical genetic algorithm, the improved algorithm is more efficient in mobile robot path planning.