Application of new combination algorithm in robot path planning

doi:10.3778/j.issn.1002-8331.2009.28.014

Computer Engineering and Applications ›› 2009, Vol. 45 ›› Issue (28): 48-51.DOI: 10.3778/j.issn.1002-8331.2009.28.014

• 研究、探讨 • Previous Articles Next Articles

Application of new combination algorithm in robot path planning

DUAN Ai-ling¹，DENG Gao-feng¹，ZHANG Xue-ping¹，LIU Yan-ping¹，WANG Jia-yao^1，2

1.School of Information Science and Engineering，Henan University of Technology，Zhengzhou 450050，China
2.Institute of Surveying and Mapping，PLA Information Engineering University，Zhengzhou 450050，China

Received:2008-07-18 Revised:2008-10-31 Online:2009-10-01 Published:2009-10-01
Contact: DUAN Ai-ling

新的融合算法在机器人路径规划中的应用

段爱玲¹，邓高峰¹，张雪萍¹，刘彦萍¹，王家耀^1，2

1.河南工业大学信息科学与工程学院，郑州 450050
2.解放军信息工程大学测绘学院，郑州 450050

通讯作者: 段爱玲

Abstract

Abstract: Robot path planning is an important research topic in robotics field.The paper proposes an algorithm based on the combination of Ant Colony Optimization（ACO） and Particle Swarm Optimization（PSO） for path planning.The new algorithm combines the advantages of ACO and PSO effectively and generates the distribution of the initial information for ACO by using the merits of high efficiency and concision of PSO，and then uses the advantages of parallelizability，positive feedback and solution with high accuracy of ACO to get global optimum solution.The simulation result demonstrates the effectiveness and feasibility of the proposed algorithm.

Key words: path planning, Ant Colony Optimization（ACO）, Particle Swarm Optimization（PSO）

摘要： 机器人路径规划一直是机器人学领域的一个非常重要的研究课题。提出了一种基于蚁群粒子群算法融合的机器人全局路径规划算法，该方法有效地结合了蚁群算法和粒子群算法的优点，利用粒子群算法的快速简洁等特点得到蚁群算法初始信息素分布；然后利用蚁群算法的并行性、正反馈性、求解精度高等优点，求得全局最优解。仿真实验结果证明了该方法的有效性和可行性。

关键词: 路径规划, 蚁群算法, 粒子群算法

CLC Number:

TP242.6

DUAN Ai-ling¹，DENG Gao-feng¹，ZHANG Xue-ping¹，LIU Yan-ping¹，WANG Jia-yao^1，2. Application of new combination algorithm in robot path planning[J]. Computer Engineering and Applications, 2009, 45(28): 48-51.

段爱玲¹，邓高峰¹，张雪萍¹，刘彦萍¹，王家耀^1，2. 新的融合算法在机器人路径规划中的应用[J]. 计算机工程与应用, 2009, 45(28): 48-51.

[1]	HUAI Chuangfeng, GUO Long, JIA Xueyan, ZHANG Zihao. Improved A* Algorithm and Dynamic Window Method for Robot Dynamic Path Planning [J]. Computer Engineering and Applications, 2021, 57(8): 244-248.
[2]	LIAO Liefa, LI Haohan, LI Shuai, ZHU Helong, LI Zhijun. Research on Control Strategy of Soccer Robot Combined with Winner-Take-All [J]. Computer Engineering and Applications, 2021, 57(7): 136-143.
[3]	WANG Di, LI Caihong, GUO Na, LIU Guoming, GAO Tengteng. Local Path Planning of Mobile Robot Based on Fuzzy Potential Field Method [J]. Computer Engineering and Applications, 2021, 57(6): 212-218.
[4]	HAN Xiaowei, HAN Zhen, YUE Gaofeng, CUI Jianjiang. Path Planning Algorithm of Disaster Relief UAV Based on Optimized A [J]. Computer Engineering and Applications, 2021, 57(6): 232-238.
[5]	ZHU Jiaying, GAO Maoting. AUV Path Planning Based on Particle Swarm Optimization and Improved Ant Colony Optimization [J]. Computer Engineering and Applications, 2021, 57(6): 267-273.
[6]	LIU Jianyu, FAN Pingqing. Path Planning of Manipulator Based on Improved RRT*-connect Algorithm [J]. Computer Engineering and Applications, 2021, 57(6): 274-278.
[7]	JIANG Lin, FANG Dongjun, LEI Bin, LI Weigang. Research Status and Trend of Navigation Algorithm for Mobile Robot with Monocular Vision [J]. Computer Engineering and Applications, 2021, 57(5): 1-9.
[8]	MA Xianghua, ZHANG Qian. Research on Improved Ant Colony Algorithm in Robots Path Planning [J]. Computer Engineering and Applications, 2021, 57(5): 210-215.
[9]	HU Xiaomin, WANG Mingfeng, ZHANG Shourong, LI Min. New Differential Evolution with Particle Swarm Optimization Algorithm for Text Clustering [J]. Computer Engineering and Applications, 2021, 57(4): 61-67.
[10]	LI Yuqi, LIU Zhiqian, CHENG Ningyi, WANG Yingying, ZHU Chunli. Path Planning of UAV Under Multi-constraint Conditions [J]. Computer Engineering and Applications, 2021, 57(4): 225-230.
[11]	WANG Jiayue, SHI Li. Research on Path Optimization and Charging Strategy of Electric Refrigerated Vehicle Considering Cost of Battery Capacity Degradation [J]. Computer Engineering and Applications, 2021, 57(24): 283-289.
[12]	YANG Lingyao, ZHANG Aihua, ZHANG Jie, SONG Jiqiang. Real-Time Path Planning of Velocity Potential for Robot in Grid Map Environment [J]. Computer Engineering and Applications, 2021, 57(24): 290-295.
[13]	GU Haiyan, CHEN Liang, WANG Duodian. Space-Time Cooperative Path Planning for Multi-UAV Using Model Predictive Control [J]. Computer Engineering and Applications, 2021, 57(23): 270-279.
[14]	WANG Chen, MAO Jian. Path Planning Method of Bidirectional Robot Based on Time Window Model [J]. Computer Engineering and Applications, 2021, 57(23): 287-294.
[15]	LIN Weixing, WANG Yujia, CHEN Wanfen, LIANG Haina. High-Dimensional Data Feature Selection Algorithm Based on Multifactor Particle Swarm Optimization [J]. Computer Engineering and Applications, 2021, 57(22): 199-207.

Application of new combination algorithm in robot path planning

新的融合算法在机器人路径规划中的应用

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics