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智能仿生算法在移动机器人路径规划优化中的应用综述
引用本文:于振中,李 强,樊启高.智能仿生算法在移动机器人路径规划优化中的应用综述[J].计算机应用研究,2019,36(11).
作者姓名:于振中  李 强  樊启高
作者单位:江南大学物联网工程学院,江苏无锡,214000
基金项目:国家自然科学基金资助项目(51405198);江苏省自然科学基金资助项目(BK20130159)
摘    要:随着移动机器人应用领域的扩大和工作环境的复杂化,传统路径规划算法因其自身局限性变得难以满足人们的要求。近年来,智能仿生算法因其群集智慧和生物择优特性而被广泛应用于移动机器人路径规划优化中。首先,按照智能仿生算法仿生机制的来源,对应用于路径规划优化中的智能仿生算法进行了分类。然后,按照不同的类别,系统的叙述了各种新型智能仿生算法在路径规划优化中取得的最新研究成果,总结了路径规划优化过程中存在的问题以及解决方案,并对算法在路径规划优化中的性能进行了比较分析。最后对智能仿生算法在路径规划优化中的研究方向进行了探讨。

关 键 词:智能仿生算法  仿生机制  移动机器人  路径规划优化  收敛速度  局部最优
收稿时间:2018/7/27 0:00:00
修稿时间:2019/10/1 0:00:00

Survey on application of bioinspired intelligent algorithms in path planning optimization of mobile robots
Yu Zhenzhong,Li Qiang and Fao Qigao.Survey on application of bioinspired intelligent algorithms in path planning optimization of mobile robots[J].Application Research of Computers,2019,36(11).
Authors:Yu Zhenzhong  Li Qiang and Fao Qigao
Abstract:With the expansion of the fields of mobile robot applications and the complication of its working environment, traditional path planning algorithms become difficult to meet people''s requirements due to their own limitations. In recent years, bioinspired intelligent algorithms(BIAs) are widely used in mobile robot path planning optimization because of its collective intelligence of the group and the characteristics of biological preference. First, this paper classified the bioinspired intelligent algorithms used in the path planning optimization according to their sources of the biomimetic mechanism. Then, to optimize the latest research results and summarize existing problems and solutions, it described systematically various new bioinspired intelligent algorithms used in the path planning optimization according to different categories, and also provided a detailed comparison and analysis of the algorithms performance in path planning optimization. Finally, it discussed further research direction about bioinspired intellgent algorithms.
Keywords:bioinspired intelligent algorithms  biomimetic mechanism  mobile robot  path planning optimization  convergence speed  local optimal
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