[1]张兴国,周东健,李成浩.基于粒子群-蚁群融合算法的移动机器人路径优化规划[J].江西师范大学学报(自然科学版),2014,(03):274-277.
　ZHANG Xing-guo,ZHOU Dong-jian,LI Cheng-hao.The Optimal Path Planning for Mobile Robot Based on Ant Colony Algorithm Combined with Particle Swarm Optimization[J].Journal of Jiangxi Normal University:Natural Science Edition,2014,(03):274-277.
点击复制

基于粒子群-蚁群融合算法的移动机器人路径优化规划()
分享到：

《江西师范大学学报》（自然科学版）[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2014年03期
页码:
274-277
栏目:
出版日期:
2014-06-30

文章信息/Info

Title:
The Optimal Path Planning for Mobile Robot Based on Ant Colony Algorithm Combined with Particle Swarm Optimization
作者:
张兴国;周东健;李成浩
南通大学机械工程学院,江苏 南通,226019
Author(s):
ZHANG Xing-guo；ZHOU Dong-jian；LI Cheng-hao
关键词:
蚁群算法; 粒子群算法; TSP问题; 路径规划; 移动机器人
Keywords:
ant colony algorithm; particle swarm optimization; TSP problem; path planning; mobile robot
分类号:
TP242
文献标志码:
A
摘要:
基于TSP问题，提出了一种基于粒子群-蚁群算法相互融合的综合优化算法对移动机器人路径规划问题进行研究。通过粒子群算法对全局路径实施粗略搜索，获得部分次优解，在获得次优解的路径上进行信息素分布，再采用蚁群算法进行精确搜索，得到路径规划的最优解。实验结果表明：粒子群-蚁群融合优化算法在路径寻优上优于蚁群算法及粒子群算法。
Abstract:
Aiming at the TSP problem,in order to research the optimal path planning for mobile robot,a new algo-rithm based on ant colony algorithm combined with particle swarm algorithm( PAAAA)has been proposed. Firstly, using the particle swarm optimization to search the global path,the second best solution is obtained. Then,after dis-tributing the pheromones on the second best solution paths,using ant colony algorithm to finish accurate searching. Last,the optimal solution of path planning is achieved. The simulation result shows that PAAA is better than single ant colony algorithm or single particle swarm optimization.

参考文献/References:

[1] 薛颂东,曾建潮.群机器人研究综述 [J].模式识别与人工智能,2008,21(2):177-185.
[2] Montiel-Ross O,Sepulveda R,Castillo O,et al.Ant colony test center for planning autonomous mobile robot navigation [J].Computer Application in Engineer Education,2013,21(2):214-229.
[3] Roberge V,Tarbouchi M,Labonte G.Comparison of parallel genetic algorithm and particle swarm optimization for real-time UAV path planning [J].IEEE Transactions on Industrial Informatics,2012,9(1):132-141.
[4] Tan Jingjing,Zhao Ping.Advances in biomedical engineering-2012 international conference on environmental engineering and technology(ICEET2012)[C].Hong Kong:Information Engineering Research Institute,2012.
[5] Chang Qingliang,Zhou Huaqiang,Hou Chaojiong.Using particle swarm optimization algorithm in an artificial neural network to forecast the strength of paste filling material [J].Journal of China University of Mining & Technology,2008(4):551-555.
[6] 陈晓娥,苏理.一种基于环境栅格地图的多机器人路径规划方法 [J].机械科学与技术,2009,28(10):1335-1139.
[7] Liu Jingfa,Li Gang,Geng Huantong.A new heuristic algorithm for the circular packing problem with equilibrium constraints [J].Science China:Information Sciences,2011,54(8):1572-1575.
[8] 张频捷.蚁群优化算法及其应用研究 [D].长沙:中南大学,2010.
[9] 禹旺明,熊红云.改进的蚁群算法在TSP中的应用 [J].现代物流技术,2009(1):27-29.
[10] 卞锋.粒子群优化算法在TSP中的研究及应用 [D].无锡:江南大学,2008.
[11] 苏晋荣,王建珍.改进粒子群优化算法求解TSP问题 [J].计算机工程与应用,2010,46(4):52-54.
[12] 姚兴田,吴亮亮,马永林.自动3维重构中确定下一最优视点的方法研究 [J].江西师范大学学报:自然科学版,2013,37(6):569-573.
[13] 张磊,张兴国.基于李群代数表达帧间位姿变化矩阵的3D视觉跟踪研究 [J].江西师范大学学报:自然科学版,2012,36(5):466-471.
[14] 徐雪松.复杂环境中移动机器人路径规划 [J].江西师范大学学报:自然科学版,2014,38(1):83-88.

相似文献/References:

[1]何文译,林鸿飞,杨亮.基于群体智慧的电影排序模型[J].江西师范大学学报(自然科学版),2013,(02):136.
　HE Wen-yi,LIN Hong-fei,YANG Liang.A Movies Ranking Model Based on Collective Intelligence[J].Journal of Jiangxi Normal University:Natural Science Edition,2013,(03):136.
[2]于国龙,崔忠伟,左 羽.基于离散粒子群优化的MPSoC节能调度算法[J].江西师范大学学报(自然科学版),2016,40(03):307.
　YU Guolong,CUI Zhongwei,ZUO Yu.MPSoC Energy Saving Scheduling Algorithm Based on Discrete Particle Swarm Optimization[J].Journal of Jiangxi Normal University:Natural Science Edition,2016,40(03):307.

备注/Memo

备注/Memo:
江苏省自然科学基金(BK20131205)
更新日期/Last Update: 1900-01-01