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基于运动控制和频域分析的移动机器人能耗最优轨迹规划

高志伟 代学武 郑志达

高志伟, 代学武, 郑志达. 基于运动控制和频域分析的移动机器人能耗最优轨迹规划. 自动化学报, 2020, 46(5): 934-945. doi: 10.16383/j.aas.c180399
引用本文: 高志伟, 代学武, 郑志达. 基于运动控制和频域分析的移动机器人能耗最优轨迹规划. 自动化学报, 2020, 46(5): 934-945. doi: 10.16383/j.aas.c180399
GAO Zhi-Wei, DAI Xue-Wu, ZHENG Zhi-Da. Optimal Energy Consumption Trajectory Planning for Mobile Robot Based on Motion Control and Frequency Domain Analysis. ACTA AUTOMATICA SINICA, 2020, 46(5): 934-945. doi: 10.16383/j.aas.c180399
Citation: GAO Zhi-Wei, DAI Xue-Wu, ZHENG Zhi-Da. Optimal Energy Consumption Trajectory Planning for Mobile Robot Based on Motion Control and Frequency Domain Analysis. ACTA AUTOMATICA SINICA, 2020, 46(5): 934-945. doi: 10.16383/j.aas.c180399

基于运动控制和频域分析的移动机器人能耗最优轨迹规划

doi: 10.16383/j.aas.c180399
基金项目: 

国家自然科学基金 61773111

国家自然科学基金 61790574

详细信息
    作者简介:

    高志伟 东北大学流程工业综合自动化国家重点实验室硕士研究生. 2016年获东北大学控制工程学院学士学位.主要研究方向为移动机器人路径规划与运行控制. E-mail: kinggzw@163.com

    郑志达 东北大学流程工业综合自动化国家重点实验室硕士研究生.主要研究方向为无线网络控制系统的故障检测. E-mail: zheng zhida@126.com

    通讯作者:

    代学武 东北大学流程工业综合自动化国家重点实验室教授.主要研究方向为动态系统鲁棒状态估计, 无线传感测量与控制、状态监测方面的工作, 及其在工业物联网, 高铁调度等领域的应用.本文通信作者.E-mail: daixuewu@mail.neu.edu.cn

Optimal Energy Consumption Trajectory Planning for Mobile Robot Based on Motion Control and Frequency Domain Analysis

Funds: 

National Natural Science Foundation of China 61773111

National Natural Science Foundation of China 61790574

More Information
    Author Bio:

    GAO Zhi-Wei Master student at the State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University. He received his bachelor degree from Northeastern University in 2016. His research interest covers mobile robots path planning and operation control

    ZHENG Zhi-Da Master student at the State Key Laboratory of Synthetical Automation for Process Industries at Northeastern University. His main research interest is fault detection of wireless network control system

    Corresponding author: DAI Xue-Wu Professor at the State Key Laboratory of Synthetical Automation for Process Industries at Northeastern University. His research interest covers robust state estimation and condition monitoring of industrial systems, wireless sensor actuator networks, industrial internet of things, networked control systems and train rescheduling. Corresponding author of this paper
  • 摘要: 本文针对两轮自平衡可移动机器人, 提出了一种新的能耗最优运动轨迹规划方法.本文将轨迹规划与由轨迹跟踪控制器和机器人动力学方程组成的运动控制模型相结合, 基于期望轨迹与实际电机输入电压间的传递函数和能量在时域和频域上的对应关系, 通过频域分析的方法得到了具有明确机理表达的线性能耗模型, 并采用最小二乘线性回归法对模型参数进行辨识.对于能耗最优轨迹, 由全局路径规划得到的路径点作为局部轨迹规划的局部目标点, 通过一定的数学转换和参数求导, 可直接得到相邻两个局部目标点间的能耗最优运行轨迹和对应的运行时间.通过仿真实验证明了本文所提能耗模型的准确性和所得轨迹的能耗最优性.
    Recommended by Associate Editor HE Wei
    1)  本文责任编委 贺威
  • 图  1  机器人实际路径与期望路径对比图以及对应的控制器动态过程示意图

    Fig.  1  The actual path of the robot is compared with the expected path and the controller dynamic process

    图  4  机器人运行轨迹示意图

    Fig.  4  Diagram of robot navigation trajectory

    图  2  两轮自平衡小车侧视图与俯视图

    Fig.  2  The side view and top view of two-wheel self-balancing robot

    图  3  本文采用的轨迹规划和控制系统体系结构

    Fig.  3  Architecture of the mobile robot trajectory planning and control system in this paper

    图  5  能耗模型验证集拟合效果误差图

    Fig.  5  Modeling errors of energy consumption model in validation set

    图  6  全局路径

    Fig.  6  Global path

    图  7  能耗最优期望轨迹与实际轨迹对比图

    Fig.  7  Comparison diagram of the optimal energy consumption desired trajectory and actual trajectory

    图  8  能耗最优期望轨迹与实际轨迹坐标误差图

    Fig.  8  Coordinate error graph of the optimal energy consumption desired trajectory and actual trajectory

    图  9  真实能耗与运行时间和轨迹圆心角关系图

    Fig.  9  The relational graph of real energy consumption with running time and track circle angle

    图  10  三种运行轨迹对比图

    Fig.  10  Contrast figure of three kinds of running trajectory

    图  11  三种运行轨迹对比图

    Fig.  11  Comparison of three kinds of running trajectory

    表  1  两轮自平衡机器人模型参数

    Table  1  Two-wheel self-balancing robot model parameters

    机器人参数 参数值
    $M$ 机器人质量 3.2 (kg)
    $R$ 车轮半径 0.15 (m)
    $W$ 车体宽度 0.35 (m)
    $f_m$ 电机与轮轴摩擦系数 0.0022
    $f_w$ 车轮与地面摩擦系数 0.035
    $R_m$ 电机内阻 6.69 ($\Omega$)
    $P_s$ 非机械元件功率 6 (w)
    下载: 导出CSV

    表  2  两轮自平衡机器人初始能耗模型参数

    Table  2  Initial energy consumption model parameters of two-wheel self-balancing robot

    参数 数值 参数 数值 参数 数值
    $a1$ 172.2 $a6$ 1.37 $a11$ 4.27
    $a2$ 7.57 $a7$ 16.26 $a12$ 1.09
    $a3$ 13.2 $a8$ $-$4.39 $a13$ 5.82
    $a4$ $-$0.66 $a9$ $-$1.11 $b1$ 20.83
    $a5$ $-$0.94 $a10$ 15.97 $b2$ 0.93
    下载: 导出CSV

    表  3  能耗最优轨迹仿真结果

    Table  3  Energy consumption optimal trajectory simulation results

    轨迹序列 分段能耗$E_{\rm {total}}$ (J) 运行时间$T$(s) 线速度$v$ (m/s) 角速度$w $(rad/s) 方向角改变$\sigma$ (°)
    1 336.97 28.46 0.499 0.011 36.28
    2 252.43 21.42 0.523 $-$0.011 $-$20.68
    3 256.15 21.38 0.526 0.017 33.06
    4 270.15 21.62 0.527 0.03 61
    下载: 导出CSV

    表  4  与其他方法仿真结果比较

    Table  4  Compare with other methods simulation results

    轨迹序列 能耗最优算法 路径最短策略 三次贝塞尔曲线
    1 336.97 338.15 345.5
    2 252.43 252.03 265.72
    3 256.15 255.89 290.72
    4 270.15 283.82 292.3
    总能耗(J) 1 115.7 1 129.8 1 203.3
    运行时间(s) 92.88 92.88 81.32
    能耗比率(%) 100 101.26 107.85
    下载: 导出CSV

    表  5  与其他方法仿真结果比较

    Table  5  Compare with other methods simulation results

    轨迹序列 能耗最优算法 路径最短策略 三次贝塞尔曲线
    1 28.43 30.811 26.03
    2 40.56 39.87 52.4
    3 33.61 36.36 35.12
    4 33.89 33.62 36.12
    5 30.91 30.93 32.33
    6 28.57 29.02 28.82
    7 24.68 24.56 25.28
    总能耗(J) 220.64 225.18 236.1
    运行时间(s) 18.6 18.6 15.3
    能耗比率(%) 100 102.06 107
    下载: 导出CSV
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  • 收稿日期:  2018-06-05
  • 录用日期:  2018-09-17
  • 刊出日期:  2020-06-01

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