[1]白金柯,吴晓娜.一种新型蚁群随机树的机器人路径规划算法[J].机械与电子,2015,(07):73-76.
 BAI Jinke,WU Xiaona.Robot-path Planning Based on Ant Colony Optimization and Rapidly-exploring Random Tree[J].Machinery & Electronics,2015,(07):73-76.
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一种新型蚁群随机树的机器人路径规划算法
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《机械与电子》[ISSN:1001-2257/CN:52-1052/TH]

卷:
期数:
2015年07期
页码:
73-76
栏目:
智能工程
出版日期:
2015-07-26

文章信息/Info

Title:
Robot-path Planning Based on Ant Colony Optimization and Rapidly-exploring Random Tree
文章编号:
1001-2257(2015)07-0073-04
作者:
白金柯吴晓娜
(河南化工职业学院,河南 郑州 450042)
Author(s):
BAI JinkeWU Xiaona
(Henan Vocational College of Chemical Technology,Zhengzhou 450042 China)
关键词:
路径规划 随机树算法 高效完备性 蚁群算法 正反馈性
Keywords:
path planning rapidly-exploring random tree efficiency and completeness ant colony optimization positive feedback
分类号:
TP302
文献标志码:
A
摘要:
为提高复杂环境下机器人的路径规划效率,提出了一种用蚁群算法来优化随机树算法的新的全局路径规划算法。该算法有效地结合了蚁群和随机树算法的优点,利用随机树算法的高效性快速收敛到一条可行路径,将该路径转换为蚁群的初始信息素分布,可以减少蚁群算法初期迭代; 然后利用蚁群算法的反馈性优化路径,求得最优路径。仿真实验表明,该蚁群随机树算法可以提高机器人路径规划的速度,并且在任何复杂环境下迅速规划出最优路径。
Abstract:
In order to improve robotic efficiency of path planning in a complex environment, this paper proposes a global path planning algorithm which makes use of ant colony optimization(ACO)to optimize rapidly-exploring random tree algorithm. The new algorithm effectively combines the strengths of the two algorithms, taking advantage of the high efficiency of the rapidly-exploring random algorithm to quickly converge a possible path, make it into a ACO initial distribution of pheromones, reduce the number of iterations and accelerate the convergence. At the same time, by using the positive feedback technology, the process of finding the optimum path is effectively improved. The simulation experiment shows that the algorithm increases the efficiency of robotic path planning greatly and can plan the best path in a complex environment quickly.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2015-01-19
基金项目:河南省基础与前沿技术研究计划项目(142300410283); 河南省教育厅科学技术研究重点项目(12B520063,14B520065); 河南省高等学校青年骨干教师资助计划项目(2013GGJS-230)
作者简介:白金柯(1984-),男,河南巩义人,助教,硕士研究生,研究方向为人工智能、路径规划。
更新日期/Last Update: 2015-07-26