关键词: 路径规划, 改进遗传算法, 模拟退火, 自调整策略, 平滑处理, 多指标规划

Abstract: An improved adaptive genetic algorithm for robot path planning is proposed. Firstly, the simulated annealing idea is introduced in the selection operation of genetic algorithm to improve the global search ability of the algorithm. Secondly, the crossover and mutation operator self-adjustment strategy is improved to improve the convergence speed of the algorithm. Then, the planned path is smoothed and optimized, and different speed segments are divided according to the distance between the path and the obstacle. Finally, multiple planning indicators such as safe driving speed and turning times are added to the fitness function to make the planned path safer and more efficient. Simulation experiments show that the improved algorithm has good implementation efficiency, high security and reliability, and the planned path is more in line with the actual situation.

Key words: path planning, improved genetic algorithm, simulated annealing, self-adjusting strategy, smooth processing, multi-indicator planning