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移动机械臂的层级聚合建模方法研究

董方方 杨超 韩江 张新荣

董方方, 杨超, 韩江, 张新荣. 移动机械臂的层级聚合建模方法研究[J]. 应用数学和力学, 2023, 44(12): 1473-1490. doi: 10.21656/1000-0887.440025
引用本文: 董方方, 杨超, 韩江, 张新荣. 移动机械臂的层级聚合建模方法研究[J]. 应用数学和力学, 2023, 44(12): 1473-1490. doi: 10.21656/1000-0887.440025
DONG Fangfang, YANG Chao, HAN Jiang, ZHANG Xinrong. A Hierarchical Aggregation Modelling Method for Mobile Manipulators[J]. Applied Mathematics and Mechanics, 2023, 44(12): 1473-1490. doi: 10.21656/1000-0887.440025
Citation: DONG Fangfang, YANG Chao, HAN Jiang, ZHANG Xinrong. A Hierarchical Aggregation Modelling Method for Mobile Manipulators[J]. Applied Mathematics and Mechanics, 2023, 44(12): 1473-1490. doi: 10.21656/1000-0887.440025

移动机械臂的层级聚合建模方法研究

doi: 10.21656/1000-0887.440025
基金项目: 

国家自然科学基金项目 52275484

安徽省自然科学基金项目 2208085ME126

详细信息
    作者简介:

    董方方(1988—),男,副教授,博士,硕士生导师(E-mail: fangfangdong@hfut.edu.cn)

    通讯作者:

    张新荣(1968—),男,教授,博士,硕士生导师(通讯作者. E-mail: zhangxinrong_chd@163.com)

  • 中图分类号: O313.3;TH113

A Hierarchical Aggregation Modelling Method for Mobile Manipulators

  • 摘要: 移动机械臂因机械臂在动态作业过程中的耦合效应会影响移动平台的运动特性,增加了整个系统的复杂度和非线性,给系统建模带来了极大挑战. 为此提出了一种新的层级聚合建模方法. 该方法依据分析力学中Udwadia-Kalaba(U-K)理论的层级属性,首先将移动机械臂划分为3个子系统,并分别利用Lagrange方程建立各自的无约束动力学模型,然后基于移动机械臂机械结构上的约束利用Udwadia-Kalaba基本方程(UKE)建立整体系统模型. 此外,针对系统存在初始条件偏差的情况,利用基于Lyapunov稳定性理论来补偿初始条件偏差,以达到收敛理想轨迹的目的. 仿真结果验证了该文所提出的建模方法的可行性.
  • 图  1  单摆系统

    Figure  1.  A single pendulum system

    图  2  移动机械臂子系统分割图

    Figure  2.  Partition of the mobile manipulator subsystem

    图  3  移动机械臂坐标示意图

    Figure  3.  Coordinate systems for the mobile manipulator

    图  4  移动平台结构示意图

    Figure  4.  Schematic diagram of the mobile platform structure

    图  5  子系统S21S31示意图

    Figure  5.  Diagrams of subsystem S21 and S31

    图  6  移动平台轨迹

    Figure  6.  Mobile platform trajectories

    图  7  机械臂各关节轨迹

    Figure  7.  Trajectories of each joint of the manipulator

    图  8  移动平台与机械臂各自轨迹误差

    Figure  8.  The respective trajectory errors of the mobile platform and the manipulator

    图  9  层级聚合方法与拉氏方法计算效率对比

    Figure  9.  Comparison of computation efficiency between the hierarchical aggregation method and the Lagrange method

    图  10  移动平台和末端执行器的轨迹

    Figure  10.  The mobile platform and the end-effector trajectories

    图  11  移动平台各方向轨迹图

    Figure  11.  Trajectory diagram for mobile platform in all directions

    图  12  移动平台和末端执行器的轨迹误差

    Figure  12.  The mobile platform and end-effector trajectory errors

    图  13  移动平台和关节的约束力

    Figure  13.  The mobile platforms and joint constraints

    表  1  机械臂-移动平台质量比[1]

    Table  1.   Robotic arm-mobile platform mass ratios[1]

    research institution robotic arm mass mr/kg platform mass mp/kg mass ratio δ
    University of Texas[2] 3.68 17.25 0.21
    Ryerson University[3] 15.50 44.40 0.35
    Hokkaido University[4] 4.40 20.56 0.21
    Pukyong National University[5] 2.85 9.50 0.30
    Iran University of Science and Technology[6] 0.72 6.00 0.12
    Institute of Automation, CAS[7] 7.50 60.00 0.13
    South China University of Technology[8] 2.00 10.00 0.20
    下载: 导出CSV

    表  2  机械臂几何参数

    Table  2.   Geometric parameters of the manipulator

    joint number αi-1/(°) ai-1 di θi
    1 0 0 0 θ1
    2 90° 0 l1 θ2
    3 0 l2 0 θ3
    4 0 l3 0 0
    下载: 导出CSV

    表  3  系统动力学参数表

    Table  3.   System dynamics parameters

    object mass m/kg length l/m moment of inertia I/(kg·m2)
    mobile platform 50 2
    joint 1 2 0.5 0.625
    joint 2 3 0.7 0.122 5
    joint 3 2 0.5 0.042
    end-effector 0.5 0.1 0.000 4
    下载: 导出CSV

    表  4  约束条件参数

    Table  4.   Constraint parameters

    constraint initial condition
    $\begin{gathered}F_1: x(t)=\frac{\pi}{2} t; F_2: y(t)=\sin \left(\frac{\pi}{2} t\right); \\ F_3: \varphi(t)=\cos \left(\frac{\pi}{2} t\right); \\ F_4: x_{\mathrm{mp}}(t)=0.75+0.25 \cos (\pi t); \\ F_5: y_{\mathrm{mp}}(t)=\frac{0.25 \sqrt{2}}{2} \sin (\pi t); \\ F_6: z_{\mathrm{mp}}(t)=0.25-\frac{0.25 \sqrt{2}}{2} \sin (\pi t)\end{gathered}$ $\begin{gathered}x_0=1; y_0=1; \varphi_0=0; \\ \theta_1=-\frac{\pi}{2}; \theta_2=\frac{\pi}{2}; \theta_3=\frac{\pi}{6}; \\ \dot{x}_0=1; \dot{y}_0=1; \dot{\varphi}_0=0; \\ \dot{\theta}_1=-\frac{\pi}{2}; \dot{\theta}_2=\frac{\pi}{2}; \dot{\theta}_3=\frac{\pi}{6}\end{gathered}$
    下载: 导出CSV
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出版历程
  • 收稿日期:  2023-02-02
  • 修回日期:  2023-08-06
  • 刊出日期:  2023-12-01

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