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基于人工蜂群算法的双机器人路径规划分析

王郑拓, 冯振礼, 叶国云, 徐月同, 傅建中

王郑拓, 冯振礼, 叶国云, 徐月同, 傅建中. 基于人工蜂群算法的双机器人路径规划分析[J]. 焊接学报, 2015, 36(2): 97-100.
引用本文: 王郑拓, 冯振礼, 叶国云, 徐月同, 傅建中. 基于人工蜂群算法的双机器人路径规划分析[J]. 焊接学报, 2015, 36(2): 97-100.
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WANG Zhengtuo, FENG Zhenli, YE Guoyun, XU Yuetong, FU Jianzhong. Analysis of dual-robot path planning based on artificial bee colony algorithm[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2015, 36(2): 97-100.
Citation: WANG Zhengtuo, FENG Zhenli, YE Guoyun, XU Yuetong, FU Jianzhong. Analysis of dual-robot path planning based on artificial bee colony algorithm[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2015, 36(2): 97-100.
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基于人工蜂群算法的双机器人路径规划分析

  • 1.

    浙江大学流体动力与机电系统国家重点实验室, 杭州 310027

  • 2.

    宁波如意股份有限公司, 宁波 315600

基金项目: 宁波市重大科技攻关资助项目(2012B10043);宁波市科技计划资助项目(2013B82001)

Analysis of dual-robot path planning based on artificial bee colony algorithm

  • 1.

    State Key Lab of Fluid Power Transmission and Control, Zhejiang University, Hangzhou 310027, China

  • 2.

    Ningbo Ruyi Joint Stock Co., Ltd., Ningbo 315600, China

摘要

    摘要
  • 摘要: 针对手动液压搬运车车架主焊工序双机器人同步焊接路径规划问题,文中引入虚拟点将多旅行商问题转化为单旅行商问题,选用换位表达编码方式对车架焊缝编码,然后采用基于状态转移策略的人工蜂群算法建立双机器人同步焊接数学模型,仿真求解全局最优焊接路径的较好近似解,并与改进的自适应遗传算法以及人工鱼群算法做了仿真对比试验.结果表明,人工蜂群算法不会过早停滞,具有较快收敛速度,较其它两种算法更能缩短焊接工时,基于人工蜂群算法的双机器人路径规划方法能有效解决搬运车车架主焊工序双机器人同步焊接问题.
    Abstract: [HJ1.13mm]
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    • 参考文献(10)
  • [1] 俞庆生,林冬梅,王东.多旅行商问题研究综述[J].价值工程, 2012, 31(2):166-168. Yu Qingsheng, Lin Dongmei, Wang Dong. An overview of multiple traveling salesman problem[J]. Value Engineering, 2012, 31(2):166-168.
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    [3] 黄健康,张刚,樊丁,等.基于动态模糊神经网络的铝合金脉冲MIG焊接过程解耦控制分析[J].焊接学报, 2013, 34(9):43-47. Huang Jiankang, Zhang Gang, Fan Ding, et al. Decoupling control analysis of aluminum alloy pulse MIG welding process based on dynamic fuzzy neural networks[J]. Transactions of the China Welding Institution, 2013, 34(9):43-47.
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    [8] 胡中华,赵敏.基于人工蜂群算法的TSP仿真[J].北京理工大学学报, 2009, 29(11):978-982. Hu Zhonghua, Zhao Min. Simulation on traveling salesman problem(TSP) based on artificial bees colony algorithm[J]. Transactions of Beijing Institute of Technology, 2009, 29(11):978-982.
    [9] 任子武,伞冶.自适应遗传算法的改进及在系统辨识中应用研究[J].系统仿真学报, 2006, 18(1):41-43, 66. Ren Ziwu, San Ye. Improved adaptive genetic algorithm and its application research in parameter identification[J]. Journal of System Simulation, 2006, 18(1):41-43,66.
    [10] 周永权,谢竹诚.求解TSP的改进人工鱼群算法[J].系统工程与电子技术, 2009, 31(6):1458-1461. Zhou Yongquan, Xie Zhucheng. Improved artificial fish-school swarm algorithm for solving TSP[J]. Systems Engineering and Electronics, 2009, 31(6):1458-1461.
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  • 收稿日期:  2013-07-18

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