摘要
针对传统遗传算法进化速度慢、容易陷入局部最优点等缺陷,提出了改进后新的路径规划算法。在判断路径中,基于闵科夫斯基原理对障碍物进行扩展;在构造路径中基于可视图原理进行改进,构造机器人的真正可行区域;在最短路径中对遗传算法中种群的初始化,个体的编码方法等问题做了详细的研究,并在选择算子中引入相似度的概念,大大扩大了初始种群的范围,避免进入局部最优点。最后通过仿真实验验证了此算法的可行性。
The traditional genetic algorithm has the faults of slow evolving speed being easy to get into local optimum etc.,an improved one based on it is provided.The improved one extends the barriers based on the principle of Minkowshi theory in the step of estimating the path and improves the graphic theory to construct the real feasible region.In the step of calculating the shortest path,population initialization individual coding method and so on are researched and the concept of similarity in the selection operator is imported which can enlarge the range of the early population and to avoid to get into the local optimum.At last,simulation test proves that the improved algorithm is feasible.
出处
《图学学报》
CSCD
北大核心
2012年第3期41-45,共5页
Journal of Graphics
基金
广东省科技计划资助项目(B01-D7041010)
华南理工大学高水平大学建设重点资助项目
关键词
计算机应用
遗传算法
排爆机器人
路径规划
computer application
genetic algorithm
mobile robot
path planning