改进蚁群算法的机器人路径规划研究

doi:10.16180/j.cnki.issn1007-7820.2020.01.003

电子科技 ›› 2020, Vol. 33 ›› Issue (1): 13-18.doi: 10.16180/j.cnki.issn1007-7820.2020.01.003

改进蚁群算法的机器人路径规划研究

刘永建¹,曾国辉¹,黄勃¹,李晓斌²

^1. 上海工程技术大学电子电气工程学院,上海 201620
^2. 上海应用技术大学电气与电子工程学院,上海 200235

收稿日期:2018-12-17 出版日期:2020-01-15 发布日期:2020-03-12
作者简介:刘永建(1992-),男,硕士研究生。研究方向:机器人路径规划。|曾国辉(1975-),男,博士,副教授。研究方向:电力电子系统及其控制。|黄勃(1985-),男,博士,讲师。研究方向:大数据,人工智能。
基金资助:
国家自然科学基金(61603242);江西省经济犯罪侦查与防控技术协同创新中心开放课题(JXJZXTCX-030);机械电子工程学科建设项目(2018xk-A-03)

Research on Robot Path Planning Based on Improved Ant Colony Algorithm

LIU Yongjian¹,ZENG Guohui¹,HUANG Bo¹,LI Xiaobin²

^1. School of Electronic and Electrical Engineering,Shanghai University of Engineering Science,Shanghai 201620,China
^2. School of Electrical and Electronic Engineering,Shanghai Institute of Technology,Shanghai 200235,China

Received:2018-12-17 Online:2020-01-15 Published:2020-03-12
Supported by:
National Natural Science Foundation of China(61603242);Open Task of Jiangxi Province Economic Crime Detection and Prevention Technology Collaborative Innovation Center(JXJZXTCX-030);Mechanical and Electronic Engineering Discipline Construction Project(2018xk-A-03)

摘要/Abstract

摘要：

针对传统蚁群算法存在算法收敛速度慢、易陷入局部最优的问题,文中提出了一种改进的蚁群算法。在传统A ^*算法的基础上,改进其估价函数,并将其引入到蚁群算法中,提出了改进启发函数η,增加目标点对路径搜索的吸引力,提高了收敛速度。新方法还改进了信息素挥发因子ρ,使信息素挥发因子处于动态变化,提高了算法的全局搜索能力,避免陷入局部最优。仿真结果表明,改进的蚁群算法在收敛速度上比传统蚁群算法提高了近50%,在最短路径上明显优于传统的蚁群算法,证明了改进算法的有效性。

关键词: 蚁群算法, A ^*算法, 机器人, 启发因子, 信息素挥发因子, 路径规划

Abstract:

Aiming at the problem that the traditional ant colony algorithm had slow convergence speed and easy to fall into local optimum, an improved ant colony algorithm was proposed. Based on the traditional A ^* algorithm, the valuation function of the traditional A ^* algorithm was improved. which was further introduced into the ant colony algorithm. The modified heuristic function η was proposed to increase the attraction of the target point to the path search and improve the convergence speed. The pheromone volatilization factor ρ was improved, and the pheromone volatilization factor was dynamically changed, which promoted the global search ability of the algorithm and prevent it from falling into local optimum. The simulation results showed that the improved ant colony algorithm was nearly 50% faster than the traditional ant colony algorithm in convergence rate, and was superior to the traditional ant colony algorithm in the shortest path, which proved the effectiveness of the improved algorithm.

Key words: ant colony algorithm, A ^* algorithm, robot, heuristic factor, pheromonevolatil, path planning

中图分类号:

TP242.6

刘永建,曾国辉,黄勃,李晓斌. 改进蚁群算法的机器人路径规划研究[J]. 电子科技, 2020, 33(1): 13-18.

LIU Yongjian,ZENG Guohui,HUANG Bo,LI Xiaobin. Research on Robot Path Planning Based on Improved Ant Colony Algorithm[J]. Electronic Science and Technology, 2020, 33(1): 13-18.

图/表 9

图1

图2

图3

图4

图5

图6

图7

表1

表2

参考文献 18

[1]	Ge Junxiang, Sun Funchun, Liu Chunfang. An efficient rapidly-exploring random tree approach with goal directionality for redundant manipulator path planning[C]. Qingdao:IEEE International Conference on Robotics and Biomimetics, 2016.
[2]	Liu Kun, Zhang Mengxia . Path planning based on simulated annealing ant colony algorithm[C]. Hangzhou: 9 ^th International Symposium on Computational Intelligence and Design ( ISCID), 2016.
[3]	Wang Wenrui, Zhu Mingchao, Wang Xiaoming , et al. An improved artificial potential field method of trajectory manipulators[J]. International Journal of Advanced Robotic Systems, 2018,15(5):1-13.
[4]	李怡弘, 裘炅 . 粒子群蚁群融合算法的火灾救援路径研究[J]. 电子科技, 2018,31(1):58-63.
	Li Yihong, Qiu Jiong . Research on fire rescue path of particle swarm ant colony fusion algorithm[J]. Electronic Science and Technology, 2018,31(1):58-63.
[5]	Calik, Seda Kazdal. UAV path planning with multiagentant colony system approach[C]. Zonguldak:24 ^th Signal Processing and Communication Application Conference , 2016 .
[6]	赵晓, 王铮, 黄程侃 . 基于改进A^*算法的移动机器人路径规划[J]. 机器人, 2018,40(6):903-911.
	Zhao Xiao, Wang Wei, Huang Chengwei . Path planning of mobile robot based on improved A^*algorithm[J]. Robotics, 2018,40(6):903-911.
[7]	Cheng Juntao, Miao Zhihuai, Li Bing , et al. An improved ACO algorithm for mobile robot path planning[C]. Ningbo:IEEE International Conference on Information and Automation(ICIA), 2016.
[8]	葛勇, 牛成水, 杜美云 , 等. 基于改进遗传算法的移动机器人路径规划的研究[J]. 青海大学学报, 2018,36(2):33-41.
	Ge Yong, Niu Chengshui, Du Meiyun , et al. Research on path planning of mobile robot based on improved genetic algorithm[J]. Journal of Qinghai University, Apr. 2018,36(2):33-41.
[9]	周敬东, 郑晓玄 . 基于改进蚁群算法的路径规划方法[J]. 湖北工业大学学报, 2018,33(5):49-53.
	Zhou Jingdong, Zheng Xiaoxuan . Path planning method based on improved ant colony algorithm[J]. Journal of Hubei University of Technology, 2018,33(5):49-53.
[10]	朱颢东, 孙振, 吴迪 , 等. 基于改进蚁群算法的移动机器人路径规划[J]. 重庆邮电大学学报:自然科学版, 2016,28(6):849-856.
	Zhu Yidong, Sun Zhen, Wu Di , et al. Path planning of mobile robot based on improved ant colony algorithm[J]. Journal of Chongqing University of Posts and Telecommunications:Natural Science Edition, 2016,28(6):849-856.
[11]	杜博, 夏春蕾, 戴曙光 . 融合改进蚁群算法和粒子群算法的路径搜索应用[J]. 电子科技, 2016,29(9):4-8.
	Du Bo, Xia Chunlei, Dai Shuguang . Path search application based on improved ant colony algorithm and particle swarm optimization algorithm[J]. Electronic Science and Technology, 2016,29(9):4-8.
[12]	Guruji, Agarwal H , Time-efficient A^* algorithm for robot path planning[J]. Procedia Technology, 2016(23):144-149.
[13]	Wu Gengrui, Bo Niao, Wu Husheng . Fuzzy scheduling optimization system for multi-objective transportation path based on ant colony algorithm[J]. Journal of Intellgent & Fuzzy Systems, 2018,35(4):4257-4266.
[14]	刘梦佳, 向凤红, 郭宁 , 等. 改进的遗传蚁群混合算法求解多维0/1背包问题[J]. 电子科技, 2018,31(7):55-59.
	Liu Mengjia, Xiang Fenghong, Guo Ning , et al. Improved genetic ant colony hybrid algorithm for solving multidimensional 0/1 knapsack problem[J]. Electronic Science and Technology, 2018,31(7):55-59.
[15]	Liu Jianhua, Yang Jianguo, Liu Huaping . An improved ant colony algorithm for robot path planning[J]. Soft Computing, 2017,21(19):5829-5839.
[16]	Septiana Risma, Soesanti Lndah, Setiawan Noor Akhmad. Evaluation function effectiveness in Wireless Sensor Network routing using A-star algorithm[C]. Bandung:4 ^th International Conference on Cyber and IT Service Management , 2016.
[17]	Jain Vinod, Prasad, Jay Shankar . An optimized algorithm for solving travelling salesman problem using Greedy Crossover operator[C]. New Delhi:3 ^rd International Conference on Computing for Sustainable Global Development (INDIACom), 2016.
[18]	Akka Khaled, Khaber Farid . Mobile robot path planning using an improved ant colony optimization[J]. International Journal of Advanced Robotic Systems, 2018,15(3):1-7.

改进蚁群算法的机器人路径规划研究

Research on Robot Path Planning Based on Improved Ant Colony Algorithm

RichHTML

PDF (PC)

赞

可视化

摘要/Abstract

引用本文

使用本文

图/表 9

参考文献 18

相关文章 15

Metrics

本文评价

推荐阅读 0

[1]	叶恒超,程宗毛. 基于中继多跳模型的传感网络充电方法研究[J]. 电子科技, 2021, 34(8): 31-36.
[2]	水九生,王然. 一种按需多节点优先级充电调度算法[J]. 电子科技, 2021, 34(8): 43-49.
[3]	章豪,王然. 基于定向充电模型的最大化网络累计充电增益[J]. 电子科技, 2021, 34(6): 67-72.
[4]	胡琼琼,伞红军,陈久朋,谢飞亚,陈中平,李鹏宇. 一种四自由度并联机器人的运动性能分析及仿真[J]. 电子科技, 2021, 34(11): 46-54.
[5]	陈明方,张凯翔,陈久朋,熊彬洲,李奇,姚国一,李鹏宇. 基于MATLAB Robotics Toolbox的四足机器人轨迹仿真与优化[J]. 电子科技, 2020, 33(9): 31-37.
[6]	朱宏伟,张海南. 基于拥挤度因子的动态信息素更新策略蚁群算法[J]. 电子科技, 2020, 33(8): 59-64.
[7]	韩强,李昌昊,程建鹏,张西良,李伯全,尹经天. 起重机轨道检测机器人激光姿态角变结构双闭环控制研究[J]. 电子科技, 2020, 33(7): 17-21.
[8]	陈久朋,伞红军,张道义,胡琼琼,张凯翔,熊彬州. 一种四足机器人对角步态仿真分析[J]. 电子科技, 2020, 33(5): 33-38.
[9]	张道义,伞红军,陈久朋,李鹏飞,熊彬州. 5-DOF混联雕刻机速度运动学分析及仿真[J]. 电子科技, 2020, 33(4): 42-49.
[10]	张凯翔,陈久朋,熊彬州,李奇,胡琼琼. 工业机器人空间轨迹仿真分析[J]. 电子科技, 2020, 33(10): 26-32.
[11]	刘学芳,曾国辉,黄勃,鲁敦科. 基于改进蚁群算法的移动机器人路径规划研究[J]. 电子科技, 2019, 32(9): 5-9.
[12]	陈昊,沈景凤,赵程. 无人机在抢险救灾中的优化应用[J]. 电子科技, 2019, 32(9): 20-25.
[13]	毛晨悦,吴鹏勇. 基于人工势场法的无人机路径规划避障算法[J]. 电子科技, 2019, 32(7): 65-70.
[14]	赵威,曾国辉,黄勃,朱爽鑫,刘瑾. 基于改进局部搜索算法的三维空间路径规划研究[J]. 电子科技, 2019, 32(6): 58-63.
[15]	胡浍冕. 变概率双向RRT改进路径规划算法[J]. 电子科技, 2019, 32(6): 16-21.

	传统蚁群算法	改进的蚁群算法
最小路径长度	31	30
迭代次数	33	23

	传统蚁群算法	改进的蚁群算法
最小路径长度	37	32
迭代次数	36	27