|本期目录/Table of Contents|

[1]廖卫强,周宣达.动态环境中基于遗传算法的机器人路径规划[J].集美大学学报(自然科学版),2012,17(1):60-64.
　LIAO Wei-qiangZHOU Xuan-da.Soccer Robot Path-planning Based on GeneticAlgorithms in Dynamic Environment[J].Journal of Jimei University,2012,17(1):60-64.
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动态环境中基于遗传算法的机器人路径规划(PDF)
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《集美大学学报（自然科学版）》[ISSN:1007-7405/CN:35-1186/N]

卷:
第17卷
期数:
2012年第1期
页码:
60-64
栏目:
船舶与机械工程
出版日期:
2012-01-25

文章信息/Info

Title:
Soccer Robot Path-planning Based on GeneticAlgorithms in Dynamic Environment
作者:
廖卫强; 周宣达
(集美大学轮机工程学院,福建 厦门 361021)
Author(s):
LIAO Wei-qiangZHOU Xuan-da
(Marine Engineering Institute,Jimei University,Xiamen 361021,China)
关键词:
足球机器人; 路径规划; 避障; 遗传算法
Keywords:
soccer robots; path planning; obstacleavoidance; genetic algorithms
分类号:
-
DOI:
-
文献标志码:
-
摘要:
        为解决动态环境中足球机器人的路径规划问题，采用栅格法对机器人工作空间进行划分，用序号标识栅格，并以此序号作为机器人路径规划参数编码，建立了以路径最短、避障为优化目标的遗传算法个体评价函数.采用轮盘赌选择、重合点交叉、多种变异结合等方法完成了遗传操作.针对遗传算法易陷入局部最优的不足,在标准遗传算法基础上加入了复原操作和重构操作,使改进后的遗传算法收敛于全局最优.仿真结果表明：该算法能够成功地在动态环境里规划出一条近似最优的路径，算法是有效的
Abstract:
In the dynamic environment,soccer robot dynamic path planning is a difficult problem to solve.The paper proposed a method of path planning based on genetic arithmetic.The robot was supposed to move in a two-dimensional workspace with some obstacles in it.The grids were used to discrete the two-dimensional workspace.Sequence number of the grid was used to code the moving path of the robot.The sequence number was so defined that one grid corresponded to only one sequence number.This paper presented an adaptive genetic algorithm function,by which the soccer robot could move along the shortest path and avoid obstacles.And by the roulette wheel selection,coincident-point crossover and combined mutation,the genetic operation was completed.For the disadvantage of research convergence of the previous genetic algorithm,restoration operation and reconstruction operation were added to the standard genetic algorithm to make the algorithm converge to a global optimum.This algorithm was tested in dynamic environments.The simulation experiments showed that this algorithm was able to plan a better path rapidly and thus validated the effectiveness of the proposed approach

参考文献/References:

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　LIU Wenxia,WANG Rongjie,GAO Huaitong,et al.Path Planning of Unmanned Surface Vehicle Based on Quantum Particle Swarm Optimization[J].Journal of Jimei University,2023,28(1):34.
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备注/Memo

备注/Memo:
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更新日期/Last Update: 2014-06-28