[1]户晓玲,王健安.一种多机器人分布式编队策略与实现[J].计算机技术与发展,2019,29(01):21-25.[doi:10. 3969 / j. issn. 1673-629X. 2019. 01. 005]
　HU Xiao-ling,WANG Jian-an.A Multi-robot Distributed Formation Strategy and Implementation[J].,2019,29(01):21-25.[doi:10. 3969 / j. issn. 1673-629X. 2019. 01. 005]
点击复制

一种多机器人分布式编队策略与实现()
分享到：

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
29
期数:
2019年01期
页码:
21-25
栏目:
智能、算法、系统工程
出版日期:
2019-01-10

文章信息/Info

Title:
A Multi-robot Distributed Formation Strategy and Implementation
文章编号:
1673-629X(2019)01-0021-05
作者:
户晓玲;  王健安
太原科技大学 华科学院,山西 太原,030024
Author(s):
HU Xiao-ling; WANG Jian-an
Huake College of Taiyuan University of Science and Technology,Taiyuan 030024,China
关键词:
微粒群算法;  分布式计算;  机器人编队;  队形变换
Keywords:
particle swarm optimization; distributed computation; robots formation; formation change
分类号:
TP393
DOI:
10. 3969 / j. issn. 1673-629X. 2019. 01. 005
文献标志码:
A
摘要:
多机器人编队问题已经成为当前研究的热点,在执行一些如地震救援、军事搜索等复杂任务时,多个机器人通过保持一定的队形可以提高复杂任务的执行效率.为了让机器人依据要完成的目标快速形成目标队形,选择合适的编队控制策略十分重要.对此,提出了一种新的基于微粒群模型的多机器人编队算法.该算法利用分布式计算的方式控制多机器人编队,通过构造包含所有机器人位置信息的队形适应值函数,将函数取最优解时的变量作为目标队形的位置,采用微粒群算法优化适应值函数,将优化过程中的最优解作为机器人的最优位置,使机器人朝着最优解运动,成功实现了四种目标队形(线形、三角形、圆形和六边形)的编队控制和队形变换,仿真结果证明了该算法的有效性.
Abstract:
The issue of multi-robot formation has become a hot spot in current research. When performing complex tasks such as earthquake rescue and military search,multiple robots can improve the execution efficiency of complex tasks by maintaining a certain formation. In order to quickly form a target formation based on the objectives to be achieved for robots,it is important to select the appropriate formation control strategy. For this,we propose a new multi-robot formation algorithm based on particle swarm optimization. This algorithm uses distributed computing to control the formation of multiple robots. By constructing a shape-adaptive value function that contains all robot position information,the function is optimally solved. The time variable is used as the position of the target formation. The particle swarm optimization algorithm is used to optimize the fitness function. The optimal solution in the optimization process is used as the optimal position of the robot. The robot moves toward the optimal solution and successfully achieves four target formations (linear, triangular,circular and hexagonal) formation control and formation transformation. The effectiveness of the algorithm is proved by simulation results.

相似文献/References:

[1]贾占朝 张亚鸣.基于遗传微粒群混合算法的灰度图像增强[J].计算机技术与发展,2009,(07):69.
　JIA Zhan-chao,ZHANG Ya-ming.A Gray- Image Enhancement Based GA and PSO Hybrid Algorithm[J].,2009,(01):69.
[2]户晓玲 曾建潮.基于微粒群模型的移动传感器网络部署研究[J].计算机技术与发展,2009,(10):81.
　HU Xiao-ling,ZENG Jian-chao.Deployment of Wireless Sensor Networks Mobile Nodes Based on Particle Swarm Optimization Model[J].,2009,(01):81.
[3]卢桂馥 刘金飞 王勇 窦易文.基于微粒群算法和脉冲耦合神经网络的图像分割算法[J].计算机技术与发展,2008,(07):90.
　LU Gui-fu,LIU Jin-fei,WANG Yong,et al.An Image Segmentation Method Based on PSO Algorithm and PCNN[J].,2008,(01):90.
[4]熊鹰 周树民 祁辉.求解二层规划的混合微粒群算法[J].计算机技术与发展,2007,(04):229.
　XIONG Ying,ZHOU Shu-min,QI Hui.Hybrid Particle Swarm Optimization for Bilevel Programming[J].,2007,(01):229.
[5]张丽 刘希玉.基于微粒群算法的聚类算法改进[J].计算机技术与发展,2010,(11):126.
　ZHANG Li,LIU Xi-yu.Improved Research of Clustering Algorithm Based on PSO[J].,2010,(01):126.
[6]陶元芳,刘晓光.一种应用ARPSO优化RBF神经网络的方法[J].计算机技术与发展,2014,24(11):43.
　TAO Yuan-fang,LIU Xiao-guang. A Method of Optimizing Radial Basis Function Neural Network by ARPSO[J].,2014,24(01):43.
[7]焦凯琳,于自强.智慧物流分布式计算模型与创新服务研究[J].计算机技术与发展,2019,29(01):206.[doi:10. 3969 / j. issn. 1673-629X. 2019. 01. 043]
　JIAO Kai-lin,YU Zi-qiang.Research on Distributed Computing Model and InnovativeServices about Intelligent Logistics[J].,2019,29(01):206.[doi:10. 3969 / j. issn. 1673-629X. 2019. 01. 043]

更新日期/Last Update: 2019-01-10