摘要
将一种自适应遗传算法应用于移动机器人路径规划.提出了一种基于几何避障法的初始种群产生算法;设计了基于启发式知识的交叉、变异、求精和删除算子;采用一种新的模糊逻辑控制算法自适应地调节交叉概率和变异概率;对移动机器人离线和在线规划问题进行了仿真研究.仿真结果表明:自适应遗传算法具有较快的搜索速度、较高的搜索质量以及较强的自适应能力,为移动机器人最优路径规划问题的解决提供了一种新方法.
An adaptive genetic algorithm for the optimum path planning problem of a mobile robot was proposed. The research project was carried out from four aspects： a geometry obstacle avoiding algorithm was developed to generate initial population; the crossover, mutation, improving and deletion operators which base on heuristic knowledge were designed for path planning; a new kind of fuzzy logic control algorithm was adopted to self-adaptively adjust the probabilities of crossover and mutation; simulation studies in both off-line and on-line environments were implemented. The simulation results show that the adaptive genetic algorithm has advantages such as rapid search speed, high search quality and strong self-adaptability. It is a new approach for solving the optimum path planning problem of a mobile robot.
出处
《北京科技大学学报》
EI
CAS
CSCD
北大核心
2008年第3期316-323,共8页
Journal of University of Science and Technology Beijing
基金
国家自然科学基金资助项目(No60374032)
