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              基于數據驅動的冗余機器人末端執行器位姿控制方案

              金龍 張凡 劉佰陽 鄭宇

              金龍, 張凡, 劉佰陽, 鄭宇. 基于數據驅動的冗余機器人末端執行器位姿控制方案. 自動化學報, 2024, 50(3): 518?526 doi: 10.16383/j.aas.c230273
              引用本文: 金龍, 張凡, 劉佰陽, 鄭宇. 基于數據驅動的冗余機器人末端執行器位姿控制方案. 自動化學報, 2024, 50(3): 518?526 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): 518?526 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): 518?526 doi: 10.16383/j.aas.c230273

              基于數據驅動的冗余機器人末端執行器位姿控制方案

              doi: 10.16383/j.aas.c230273
              基金項目: 國家自然科學基金 (62176109), 甘肅省自然科學基金杰出青年項目 (21JR7RA531), 中央高?;究蒲袠I務費 (lzujbky-2023-ct05, lzujbky-2023-ey07), 甘肅省教育廳優秀研究生“創新之星”項目 (2023CXZX-072), 騰訊Robotics X犀牛鳥專項研究計劃 (2021-01), 蘭州大學超算中心資助
              詳細信息
                作者簡介:

                金龍:蘭州大學信息科學與工程學院教授. 主要研究方向為神經網絡, 機器人技術和智能信息處理. 本文通信作者. E-mail: jinlongsysu@foxmail.com

                張凡:蘭州大學信息科學與工程學院碩士研究生. 主要研究方向為模型預測控制, 機器人技術和優化. E-mail: zhangfanas@foxmail.com

                劉佰陽:2023年獲得蘭州大學信息科學與工程學院碩士學位. 主要研究方向為機器人技術和神經網絡. E-mail: baiyang-liu@foxmail.com

                鄭宇:騰訊科技(深圳)有限公司Robotics X首席研究員. 主要研究方向為多體機器人系統, 機器人抓取與操作和機器人算法. 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. Corresponding 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 the School of Information Science and Engineering, Lanzhou University in 2023. His research interest covers robotics and neural network

                ZHENG Yu Principal researcher at Robotics X, Tencent Technology (Shenzhen) Company Limited. His research interest covers multibody robotic systems, robotic grasping and manipulation, and algorithms for robotics

              • 摘要: 模型未知的冗余機器人執行任務的過程中會產生較大的控制誤差, 其末端執行器的位置與姿態也需要針對不同任務進行修正. 為解決該問題, 提出一種基于數據驅動的冗余機器人末端執行器位置與姿態控制方案. 該方案使用在線學習技術, 能夠應用于模型未知的冗余機器人控制. 同時引入四元數表示法將控制機器人末端執行器姿態問題轉化為基于四元數表示的控制方法. 隨后, 設計一種神經動力學求解器對所提方案進行求解. 相關的理論分析、仿真及對比體現了所提方案的可行性、有效性與新穎性.
              • 圖  1  采用所提方案(14)實現冗余機器人末端執行器位置跟蹤與姿態保持的仿真結果

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

                圖  2  采用所提方案(14)實現冗余機器人位置與姿態跟蹤的仿真結果

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

                圖  3  基于CoppeliaSim平臺冗余機器人實現位置與姿態跟蹤的對比結果

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

                表  1  所提冗余機器人控制方案的符號含義

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

                符號含義
                $ {{\boldsymbol{\theta}}} \in {\bf{R}}^a $機器人關節角向量
                $ \dot{\boldsymbol{\theta}}\in {\bf{R}}^a $機器人關節角速度向量
                $ \dot{\boldsymbol{\theta}}^{-}(\dot{\boldsymbol{\theta}}^{+}) $關節角速度的下界(上界)
                $ {\boldsymbol r}\in {\bf{R}}^b $末端執行器的位置向量
                $ \boldsymbol{r}^{d}\in {\bf{R}}^b $末端執行器的期望位置向量
                $ \dot{\boldsymbol r}\in {\bf{R}}^b $末端執行器的速度向量
                $ \dot{\hat{\boldsymbol r}}\in {\bf{R}}^b $末端執行器的估計速度向量
                $ f(\cdot): {\bf{R}}^a \rightarrow {\bf{R}}^b $機器人非線性前向運動學映射
                $ J=\dfrac{\partial f({{\boldsymbol{\theta}}})}{\partial {{\boldsymbol{\theta}}}}\in {\bf{R}}^{b\times a} $機器人雅可比矩陣
                $ \hat{J}\in {\bf{R}}^{b\times a} $機器人估計雅可比矩陣
                $ {\dot{\hat{J}}}\in {\bf{R}}^{b\times a} $機器人估計雅可比矩陣的導數
                $ M(\boldsymbol \theta)\in {\bf{R}}^{3\times 3} $末端執行器的方向旋轉矩陣
                $ {\boldsymbol q}_{E}(\boldsymbol \theta)\in {\bf{R}}^{4} $末端執行器的方向四元數
                $ \boldsymbol{\overline{o}}(\boldsymbol \theta)\in {\bf{R}}^{5} $末端執行器的方向向量
                $ \tilde{\boldsymbol q}\in {\bf{R}}^{5} $末端執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} $$ {\boldsymbol q}_{E} $ 的雅可比矩陣
                $ G({\boldsymbol{\theta}})=\dfrac{\partial{\boldsymbol{\overline{o}}({\boldsymbol{\theta}}})}{\partial{{\boldsymbol{\theta}}}}\in {\bf{R}}^{5\times a} $$ \boldsymbol{\overline{o}}({\boldsymbol{\theta}}) $的雅可比矩陣
                $ \kappa(\boldsymbol q)=\dfrac{\partial{{\tilde{\boldsymbol q}}}}{\partial{\boldsymbol q}}\in {\bf{R}}^{5\times 4} $$ \tilde{\boldsymbol q} $ 的雅可比矩陣
                $ \boldsymbol{u}\in {\bf{R}}^a $方差為極小值的獨立同分布零均值隨機噪聲
                ${\boldsymbol{u} }_{0}\in {\bf{R} }^a$$ \boldsymbol{u} $的上界
                $ \hat{\dot{{\boldsymbol{\theta}}}}\in {\bf{R}}^a $受噪聲驅動的關節角速度
                $ \Vert \cdot \Vert_2 $向量的二范數
                $ \mathrm{tr(\cdot)} $矩陣的跡
                下載: 導出CSV

                表  2  冗余機器人不同軌跡跟蹤控制方案對比

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

                方案層級末端控制結構信息位置誤差(m)姿態誤差
                本文速度層位姿未知1.653 × 10?33.956 × 10?3
                文獻[13]速度層姿態保持未知1.056 × 10?34.635 × 10?4
                文獻[22]加速度層位置已知3.312 × 10?3
                文獻[23]加速度層位置已知1.423 × 10?3
                文獻[24]速度層位置已知2.734 × 10?3
                文獻[25]速度層位姿已知1.374 × 10?33.461 × 10?4
                下載: 導出CSV
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                        • 收稿日期:  2023-05-11
                        • 錄用日期:  2023-08-29
                        • 網絡出版日期:  2023-12-27
                        • 刊出日期:  2024-03-29

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