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基于数据驱动的冗余机器人末端执行器位姿控制方案

金龙 张凡 刘佰阳 郑宇

金龙, 张凡, 刘佰阳, 郑宇. 基于数据驱动的冗余机器人末端执行器位姿控制方案. 自动化学报, 2024, 50(3): 1001−1009 doi: 10.16383/j.aas.c230273
引用本文: 金龙, 张凡, 刘佰阳, 郑宇. 基于数据驱动的冗余机器人末端执行器位姿控制方案. 自动化学报, 2024, 50(3): 1001−1009 doi: 10.16383/j.aas.c230273
Jin Long, Zhang Fan, Liu Bai-Yang, Zheng Yu. Position and orientation control scheme for end-effector of redundant manipulators based on data-driven technology. Acta Automatica Sinica, 2024, 50(3): 1001−1009 doi: 10.16383/j.aas.c230273
Citation: Jin Long, Zhang Fan, Liu Bai-Yang, Zheng Yu. Position and orientation control scheme for end-effector of redundant manipulators based on data-driven technology. Acta Automatica Sinica, 2024, 50(3): 1001−1009 doi: 10.16383/j.aas.c230273

基于数据驱动的冗余机器人末端执行器位姿控制方案

doi: 10.16383/j.aas.c230273
基金项目: 国家自然科学基金 (62176109), 甘肃省自然科学基金杰出青年项目 (21JR7RA531), 中央高校基本科研业务费 (lzujbky-2023-ct05, lzujbky-2023-ey07), 甘肃省教育厅优秀研究生“创新之星” 项目 (2023CXZX-072), 腾讯Robotics X犀牛鸟专项研究计划 (2021-01), 兰州大学超算中心资助
详细信息
    作者简介:

    金龙:兰州大学信息科学与工程学院教授. 主要研究方向为神经网络, 机器人技术和智能信息处理. 本文通信作者. E-mail: jinlongsysu@foxmail.com

    张凡:兰州大学信息科学与工程学院硕士研究生.主要研究方向为模型预测控制, 机器人技术和最优化. E-mail: zhangfanas@foxmail.com

    刘佰阳:2023年获得兰州大学信息科学与工程学院硕士学位.主要研究方向为机器人技术和神经网络. E-mail: baiyang-liu@foxmail.com

    郑宇:腾讯科技(深圳)有限公司Robotics X首席研究员. 主要研究方向为多体机器人系统, 机器人抓取与操作和各种机器人算法. E-mail: petezheng@tencent.com

Position and Orientation Control Scheme for End-effector of Redundant Manipulators Based on Data-driven Technology

Funds: Supported by National Natural Science Foundation of China (62176109), Natural Science Foundation of Gansu Province (21JR7RA531), Fundamental Research Funds for the Central Universities (lzujbky-2023-ct05, lzujbky-2023-ey07), Education Department of Gansu Province: Excellent Graduate Student “Innovation Star” Project (2023CXZX-072), CIE-Tencent Robotics X Rhino-Bird Focused Research Program (2021-01), and Supercomputing Center of Lanzhou University
More Information
    Author Bio:

    JIN Long Professor at the School of Information Science and Engineering, Lanzhou University. His research interest covers neural networks, robotics, and intelligent information processing. Correspanding author of this paper

    ZHANG Fan Master student at the School of Information Science and Engineering, Lanzhou University. His research interest covers model predictive control, robotics, and optimization

    LIU Bai-Yang Received his master degree from Lanzhou University in 2023. His research interest covers robotics and neural network

    ZHENG Yu Principal researcher at Tencent Robotics X. His research interest covers multibody robotic systems, robotic grasping and manipulation, and various algorithms for robotics

  • 摘要: 模型未知的冗余机器人执行任务的过程中会产生较大的控制误差, 其末端执行器的位置与姿态也需要针对不同任务进行修正. 为解决该问题, 提出一种基于数据驱动的冗余机器人末端执行器位置与姿态控制方案. 该方案使用在线学习技术, 能够应用于模型未知的冗余机器人控制. 同时引入四元数表示法将控制机器人末端执行器姿态问题转化为基于四元数表示的控制方法. 随后, 设计一种神经动力学求解器对所提方案进行求解. 相关的理论分析、仿真及对比体现了所提方案的可行性、有效性与新颖性.
  • 图  1  采用所提方案(14)实现冗余机器人位置跟踪与姿态保持的仿真结果

    Fig.  1  Simulation results of the redundant manipulator using the proposed scheme (14) to achieve position tracking and orientation maintenance

    图  2  采用所提方案(14)实现冗余机器人位置与姿态跟踪的仿真结果

    Fig.  2  Simulation results of the redundant manipulator using the proposed scheme (14) to achieve position and orientation tracking

    图  3  基于CoppeliaSim平台冗余机器人实现位置与姿态跟踪的对比结果

    Fig.  3  Comparison results of the redundant manipulator achieving position and orientation tracking based on CoppeliaSim platform

    表  1  所提冗余机器人控制方案的符号含义

    Table  1  Definitions of variables of the proposed scheme for redundant manipulators

    符号含义
    $ {{\boldsymbol{\theta}}} \in {\bf{R}}^a $机器人关节角向量
    $ \dot{\boldsymbol{\theta}}\in {\bf{R}}^a $机器人关节角速度向量
    $ \dot{\boldsymbol{\theta}}^{-}(\dot{\boldsymbol{\theta}}^{+}) $关节角速度的下界(上界)
    $ {\boldsymbol r}\in {\bf{R}}^b $末端执行器的位置向量
    $ \boldsymbol{r}^{d}\in {\bf{R}}^b $末端执行器的期望位置向量
    $ \dot{\boldsymbol r}\in {\bf{R}}^b $末端执行器的速度向量
    $ \dot{\hat{\boldsymbol r}}\in {\bf{R}}^b $末端执行器的估计速度向量
    $ f(\cdot): {\bf{R}}^a \rightarrow {\bf{R}}^b $机器人非线性前向运动学映射
    $ J=\dfrac{\partial f({{\boldsymbol{\theta}}})}{\partial {{\boldsymbol{\theta}}}}\in {\bf{R}}^{b\times a} $机器人雅可比矩阵
    $ \hat{J}\in {\bf{R}}^{b\times a} $机器人估计雅可比矩阵
    $ {\dot{\hat{J}}}\in {\bf{R}}^{b\times a} $机器人估计雅可比矩阵的导数
    $ M(\boldsymbol \theta)\in {\bf{R}}^{3\times 3} $末端执行器的方向旋转矩阵
    $ {\boldsymbol q}_{E}(\boldsymbol \theta)\in {\bf{R}}^{4} $末端执行器的方向四元数
    $ \boldsymbol{\overline{o}}(\boldsymbol \theta)\in {\bf{R}}^{5} $末端执行器的方向向量
    $ \tilde{\boldsymbol q}\in {\bf{R}}^{5} $末端执行器的期望方向向量
    $ H({\boldsymbol \theta})=\dfrac{\partial{\boldsymbol q}_{E}(\boldsymbol \theta)}{\partial{\boldsymbol \theta}}\in {\bf{R}}^{4\times a} $$ {\boldsymbol q}_{E} $ 的雅可比矩阵
    $ G({\boldsymbol{\theta}})=\dfrac{\partial{\boldsymbol{\overline{o}}({\boldsymbol{\theta}}})}{\partial{{\boldsymbol{\theta}}}}\in {\bf{R}}^{5\times a} $$ \boldsymbol{\overline{o}}({\boldsymbol{\theta}}) $的雅可比矩阵
    $ \kappa(\boldsymbol q)=\dfrac{\partial{{\tilde{\boldsymbol q}}}}{\partial{\boldsymbol q}}\in {\bf{R}}^{5\times 4} $$ \tilde{\boldsymbol q} $ 的雅可比矩阵
    $ \boldsymbol{u}\in {\bf{R}}^a $方差为极小值的独立同分布零均值随机噪声
    $ {\bf{u}}_{0}\in {\bf{R}}^a $$ \boldsymbol{u} $的上界
    $ \hat{\dot{{\boldsymbol{\theta}}}}\in {\bf{R}}^a $受噪声驱动的关节角速度
    $ \Vert \cdot \Vert_2 $向量的二范数
    $ \mathrm{tr(\cdot)} $矩阵的迹
    下载: 导出CSV

    表  2  冗余机器人不同轨迹跟踪控制方案对比

    Table  2  Comparison of different trajectory tracking control schemes for redundant manipulators

    方案层级末端控制结构信息位置误差(m)姿态误差
    本文速度层位姿未知1.653 × 10−33.956 × 10−3
    文献[13]速度层姿态保持未知1.056 × 10−34.635 × 10−4
    文献[22]加速度层位置已知3.312 × 10−3N/A
    文献[23]加速度层位置已知1.423 × 10−3N/A
    文献[24]速度层位置已知2.734 × 10−3N/A
    文献[25]速度层位姿已知1.374 × 10−33.461 × 10−4
    下载: 导出CSV
  • [1] 胡静. 冗余自由度机器人的路径规划研究[硕士学位论文], 东南大学, 中国, 2017.

    Hu Jing. Research on Path Planning of Redundant Robot [Master thesis], Southeast University, China, 2017.
    [2] 李亚昕, 王国磊, 张剑辉, 田鑫亮, 安静, 陈恳. 基于碰撞反馈的冗余机器人避障规划算法. 清华大学学报(自然科学版), 2022, 62(03): 408-415 doi: 10.16511/j.cnki.qhdxxb.2021.25.022

    Li Ya-Xin, Wang Guo-Lei, Zhang Jian-Hui, Tian Xin-Liang, An Jing, Chen Ken. Obstacle avoidance alorithm for redundant robots based on collision feedback. Journal of Tsinghua University(Science and Technology), 2022, 62(03): 408-415 doi: 10.16511/j.cnki.qhdxxb.2021.25.022
    [3] 刘美娇. 空间超冗余四足爬行机器人轨迹规划方法研究[博士学位论文], 中国科学院大学, 中国, 2023.

    Liu Mei-Jiao. Research on Trajectory Planning Method of Spatial Hyper-Redundant Quadruped Crawling Robot [Ph.D. dissertation], University of Chinese Academy of Sciences, China, 2023.
    [4] 梁旭, 苏婷婷, 侯增广, 刘圣达, 章杰, 何广平. 基于变阻抗控制的冗余驱动并联机器人多目标内力优化. 自动化学报, 2023, 49(5): 1099-1115 doi: 10.16383/j.aas.c210963

    Liang Xu, Su Ting-Ting, Hou Zeng-Guang, Liu Sheng-Da, Zhang Jie, He Guang-Ping. A multi-objective internal preload optimization method of redundantly actuated parallel robots based on variable impedance control. Acta Automatica Sinica, 2023, 49(5): 1099-1115 doi: 10.16383/j.aas.c210963
    [5] 邝禹聪. 面向实验教学的六自由度机器人开发[硕士学位论文], 华南理工大学, 中国, 2017.

    Kuang Yu-Cong. Develpment of 6-DOF Robot for Experiment Teaching [Master thesis], South China University of Technology, China, 2017.
    [6] Xu Zhi-Hao, Zhou Xue-Feng, Wu Hong-Min, Li Xiao-Xiao, Li Shuai. Motion planning of manipulators for simultaneous obstacle avoidance and target tracking: An RNN approach with guaranteed performance. IEEE Transactions on Industrial Electronics, 2022, 69(4): 3887-3897 doi: 10.1109/TIE.2021.3073305
    [7] Thakar Shantanu, Rajendran Pradeep, Kabir Ariyan, Gupta Satyandra. Manipulator motion planning for part pickup and transport operations from a moving base. IEEE Transactions on Automation Science and Engineering, 2022, 19(1): 191-206 doi: 10.1109/TASE.2020.3020050
    [8] Xie Zheng-Tai, Jin Long, Luo Xin, Sun Zhong-Bo, Liu Mei. RNN for repetitive motion generation of redundant robot manipulators: An orthogonal projection-based scheme. IEEE Transactions on Neural Networks and Learning Systems, 2022 33(2): 615-628 doi: 10.1109/TNNLS.2020.3028304
    [9] 李中奇, 周靓, 杨辉. 高速动车组数据驱动无模型自适应控制方法. 自动化学报, 2023, 49(2): 437-447 doi: 10.16383/j.aas.c211068

    Li Zhong-Qi, Zhou Liang, Yang Hui. Data-driven model-free adaptive control method for high-speed electric multiple unit. Acta Automatica Sinica, 2023, 49(2): 437-447 doi: 10.16383/j.aas.c211068
    [10] Lee Uichin, Jung Gyuwon, Ma Eun-Yeol, Kim Jin-San, Kim Heepyung, Alikhanov Jumabek, Noh Youngtae, Kim Heeyoung. Toward data-driven digital therapeutics analytics: Literature review and research directions. IEEE/CAA Journal of Automatica Sinica, 2023, 10(1): 42-66 doi: 10.1109/JAS.2023.123015
    [11] 梁正平, 黄锡均, 李燊钿, 王喜瑜, 朱泽轩. 基于剪枝堆栈泛化的离线数据驱动进化优化. 自动化学报, 2023, 49(6): 1306-1325 doi: 10.16383/j.aas.c220387

    Liang Zheng-Ping, Huang Xi-Jun, Li Shen-Tian, Wang Xi-Yu, Zhu Ze-Xuan. Offline data driven evolutionary optimization based on pruning stacked generalization. Acta Automatica Sinica, 2023, 49(6): 1306-1325 doi: 10.16383/j.aas.c220387
    [12] 姜艺, 范家璐, 柴天佑. 数据驱动的保证收敛速率最优输出调节. 自动化学报, 2022, 48(4): 980-991 doi: 10.16383/j.aas.c200932

    Jiang Yi, Fan Jia-Lu, Chai Tian-You. Data-driven optimal output regulation with assured convergence rate. Acta Automatica Sinica, 2022, 48(4): 980-991 doi: 10.16383/j.aas.c200932
    [13] Fan Jia-Liang, Jin Long, Xie Zheng-Tai, Li Shuai, Zheng Yu. Data-driven motion-force control scheme for redundant manipulators: A kinematic perspective. IEEE Transactions on Industrial Informatics, 2022, 18(8): 5338-5347 doi: 10.1109/TII.2021.3125449
    [14] Xie Zheng-Tai, Jin Long, Luo Xin, Hu Bin, Li Shuai. An acceleration-level data-driven repetitive motion planning scheme for kinematic control of robots with unknown structure. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2022, 52(9): 5679-5691 doi: 10.1109/TSMC.2021.3129794
    [15] Jin Long, Zheng Xin. Neural dynamics for distributed collaborative control of manipulators with time delays. IEEE/CAA Journal of Automatica Sinica, 2022, 9(5): 854-863 doi: 10.1109/JAS.2022.105446
    [16] Ma Bo-Yu, Xie Zong-Wu, Zhan Bo-Wen, Jiang Zai-Nan, Liu Yang, Liu Hong. Actual shape-based obstacle avoidance synthesized by velocity-acceleration minimization for redundant manipulators: An optimization perspective. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2023: 1-15 doi: 10.1109/TSMC.2023.3283266, to be published
    [17] Zhang Zhi-Jun, He Hao-Tian, Deng Xian-Zhi. An FPGA-implemented antinoise fuzzy recurrent neural network for motion planning of redundant robot manipulators. IEEE Transactions on Neural Networks and Learning Systems, 2023: 1-13 doi: 10.1109/TNNLS.2023.3253801, to be published
    [18] Li Shuai, Shao Zi-Li, Guan Yong. A dynamic neural network approach for efficient control of manipulators. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2019, 49(5): 932-941 doi: 10.1109/TSMC.2017.2690460
    [19] Guo Kun-Lin, Su Hang, Yang Cheng-Guang. A small opening workspace control strategy for redundant manipulator based on RCM method. IEEE Transactions on Control Systems Technology, 2022, 30(6): 2717-2725 doi: 10.1109/TCST.2022.3145645
    [20] 张振国, 毛建旭, 谭浩然, 王耀南, 张雪波, 江一鸣. 重大装备制造多机器人任务分配与运动规划技术研究综述. 自动化学报, 2024, 50(1): 21−41

    Zhang Zhen-Guo, Mao Jian-Xu, Tan Hao-Ran, Wang Yao-Nan, Zhang Xue-Bo, Jiang Yi-Ming. A review of task allocation and motion planning for multi-robot in major equipment manufacturing. Acta Automatica Sinica, 2024, 50(1): 21−41
    [21] Khail H. Nonlinear Systems 3rd Edition. Englewood Cliffs, USA: Prentice-Hall, 2001.
    [22] Yan Jing-Kun, Jin Long, Yuan Zhan-Ting, Liu Zhi-Yi. RNN for receding horizon control of redundant robot manipulators. IEEE Transactions on Industrial Electronics, 2022, 69(2): 1608-1619 doi: 10.1109/TIE.2021.3062257
    [23] Zhang Zhi-Jun, Chen Si-Yuan, Zhu Xu-Peng, Yan Zi-Yi. Two hybrid end-effector posture-maintaining and obstacle-limits avoidance schemes for redundant robot manipulators. IEEE Transactions on Industrial Informatics, 2020, 16(2): 754-763 doi: 10.1109/TII.2019.2922694
    [24] Jin Long, Zhang Yu-Nong. G2-type SRMPC scheme for synchronous manipulation of two redundant robot arms. IEEE Transactions on Cybernetics, 2015, 45(2): 153-164 doi: 10.1109/TCYB.2014.2321390
    [25] Liu M, Shang M S. Orientation tracking incorporated multi-criteria control for redundant manipulators with dynamic neural network. IEEE Transactions on Industrial Electronics, 2014, 71(4): 3801−3810
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  • 收稿日期:  2023-05-11
  • 录用日期:  2023-08-29
  • 网络出版日期:  2023-12-27

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