[1]王艳,朱庆保.基于二进制粒子群算法的移动机器人路径规划[J].南京师范大学学报(工程技术版),2009,09(02):072-78.
　Wang Yan,Zhu Qingbao.Path Planning for Mobile Robot Based on Binary Particle Swarm Optimization[J].Journal of Nanjing Normal University(Engineering and Technology),2009,09(02):072-78.
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基于二进制粒子群算法的移动机器人路径规划
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南京师范大学学报（工程技术版）[ISSN:1006-6977/CN:61-1281/TN]

卷:
09卷
期数:
2009年02期
页码:
072-78
栏目:
出版日期:
2009-06-30

文章信息/Info

Title:
Path Planning for Mobile Robot Based on Binary Particle Swarm Optimization
作者:
王艳;朱庆保;
南京师范大学计算机科学与技术学院, 江苏南京210097
Author(s):
Wang Yan; Zhu Qingbao
School of Computer Sciences,Nanjing Normal University,Nanjing 210097,China
关键词:
移动机器人;  路径规划;  二进制编码粒子群算法;  栅格法
Keywords:
mob ile robot;  pa th planning;  binary partic le sw arm optim ization;  gr id m e thod
分类号:
TP242
摘要:
由于用PSO进行机器人路径规划的研究尚局限于用连续模型规划连续描述的环境中的路径,使算法受到一定的局限性.为此,研究了一种全新的基于栅格法的机器人路径规划二进制粒子群算法.首先用栅格法描述机器人工作环境,在此基础上,将机器人路径表示为粒子位置的二进制编码,并以路径长度为适应值,产生初始种群后,再对粒子位置和速度进行更新,经过多次迭代,即可获得从起始点到目标点的一条全局最优路径.该方法模型简单,算法复杂度低,收敛速度快,计算机仿真实验证明了该方法的有效性和先进性.
Abstract:
A t present, the study o f the robot path plann ing w ith Partic le Swa rm Optim ization ( PSO) is still con fined to use the continuous m ode l to p lan the path w ith continuous m ethod, so the a lgo rithm has ce rtain lim ita tions. Then a d isc rete b inary version of the PSO for path p lann ing of robot based on gr id m e thod is proposed. The first step of the algorithm is to m ake thew ork env ironm ent o f robot w ith g rid m ethod, based on wh ich robo t route is expressed as b ina ry code o f the partic le location, and the length o f the pa th is taken as the fitness va lue, and after the in itia l popu lation is generated, the location and veloc ity o f partic les are updated. Then the d iscrete binary version of the PSO is introduced to obta inde a optim ized path be tw een starting-po int and goa-l po int through m any iterations. This a lgor ithm is simp le in m ode,l low in a lgo rithm com plex ity, and rapid in convergence. The sim ulation results are prov ided to ver ify effectiveness and practicability o f them ethod.

参考文献/References:

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备注/Memo

备注/Memo:
基金项目: 国家自然科学基金( 60673102)和江苏省自然科学基金( BK2006218)资助项目.
通讯联系人: 朱庆保, 教授, 博士生导师, 研究方向: 人工智能与智能控制. E-m ail:zhuqingbao@ n jnu. edu. cn
更新日期/Last Update: 2013-04-23