基于改进自适应遗传算法的机器人路径规划研究

传统遗传算法的交叉和变异操作为随机操作,虽然简单,但在路径规划中却会产生不可行路径,增加运算量,影响算法的收敛速度。针对这一问题,在传统遗传算法遗传操作的基础上进行了改进,利用先验知识保证遗传操作后的种群个体为可行路径,同时提出了新的遗传参数自适应调整方式与之配合,提高了算法的寻优效率。最后,由于遗传算法容易陷入局部最优,根据模拟退火算法的Metropolis准则对经过遗传操作产生的新个体进行接受判定。通过将改进后的遗传算法与其他文献中的改进遗传算法相比较,结果表明:文中的改进遗传算法在收敛速度、优化效果以及寻优能力上都取得了明显的效果。

Research of Robot Path Planning Based on Improved Adaptive Genetic Algorithm

Although the crossover and mutation of traditional genetic algorithm are randomization and simple , they can produce infeasible path in path planning , increase amount of computation , have the impact on the convergence speed of algorithm .Aimed at this problem , genetic manipulation based on traditional genetic algorithm is improved by using prior knowledge to guarantee the feasibility of the path after genetic manipulation , at the same time, a new adaptive mode for adjusting genetic parameters is presented and matched . The search efficiency of the algorithm optimization was improved .At last, as it was easy for genetic algorithm to fall into the optimal lo-cal, the accept determination was proposed on the new units generated through genetic opperation by using the Metropolis rules based on simulated annealing algorithm .The improved genetic algorithm was compared with other genetic algorithm .The result shows that the improved genetic algorithm has obvoius better convergence speed , searching effects and optimization capabilities .