摘要
提出了一种基于粗糙集和遗传算法混合方法的机器人路径规划方法,以提高机器人路径规划的速度和准确性.首先利用粗糙集获得机器人路径的决策规则,建立初始决策表,利用粗糙集理论进行化简,获得最小决策表,从中提出最小决策规则,然后利用所得的最小决策规则训练得出一系列可行路径的集合,最后利用遗传算法对这个种群优化,获得最优行走路线.对于两种不同环境分别进行仿真实验,验证了两种方法的混合算法在提高机器人路径规划速度上的优势.
In this paper, a hybrid method of rough set and genetic algorithms is presented to raise the speed and accuracy of path planning of robot. Firstly, the decision rules are obtained based on rough set theory, and the initial decision table is established and is simplified according to the rough set theory. And the minimal decision table from which the minimal decision rules are drawn is obtained finally. And then, a series of available paths are produced by training the obtained minimal decide rule. Finally, the population of paths is optimized by using genetic algorithms, and the most excellent path is got. In two kinds of different environments, simulations are done. And the results show that the hybrid method is available in raising the speed of path planning of robot.
出处
《沈阳建筑工程学院学报（自然科学版）》
2003年第4期326-329,共4页
Journal of Shenyang Architectural and Civil Engineering University(Nature Science)
基金
辽宁省自然科学基金项目(002107)
关键词
粗糙集
遗传算法
机器人
路径规划
rough set
genetic algorithms
robot
path planning
