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Real-time path correction of industrial robots in machining of large-scale components based on model and data hybrid drive
参考中译:基于模型和数据混合驱动的大型零件加工工业机器人实时路径修正


          

刊名：Robotics and Computer-Integrated Manufacturing
作者：Yang Lin（State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology）
Huan Zhao（State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology）
Han Ding（State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology）
刊号：737C0069
ISSN：0736-5845
出版年：2023
年卷期：2023, vol.79
页码：102447-1--102447-11
总页数：11
分类号：TP24
关键词：Link position estimation；Flexible dynamics；Data-driven prediction；Path correction；Industrial robots
参考中译：链接位置估计；柔性动力学；数据驱动预测；路径修正；工业机器人
语种：eng
文摘：Industrial robots are increasingly used in machining of large-scale components due to advantages of high repeatability, large workspace and low cost. Nevertheless, applying industrial robots to high-accuracy machining of large-scale components remains a challenge, where the major hurdle is the insufficient manipulator stiffness due to joint flexibility. When the robot is performing a machining task, joint flexibility-induced position errors between motor and link called joint position errors (JPEs), as the main source of robot deformations, make the robot deviate from the desired path. For most industrial robots, due to the lack of link-side encoders, it is difficult to obtain the JPEs by direct measurement and compensate them in the controller, which deteriorates the path accuracy of the robot during machining greatly. To address this problem, this paper presents a realtime path correction approach of industrial robots based on JPE estimation and compensation with requiring only motor-side measurements and external wrenches. The proposed approach is divided into three steps. First, to estimate the actual link position of the robot in real-time, the dynamics of a manipulator with joint flexibility called flexible dynamics (FD) is introduced. Second, by taking both FD and disturbance dynamics into account, a novel link state estimator called flexible-dynamics based disturbance Kalman filter (FDBDKF) is developed, and thus JPEs can be estimated in real-time. Third, a data-driven locally weighted projection regression (LWPR)-based JPE prediction and compensation method is developed to further improve the compensation accuracy of the JPEs. Simulation and experimental results, obtained on a 6-DOF industrial robot, demonstrate the feasibility and effectiveness of the proposed approach. Experimental results show significant improvement (>80%) in the path accuracy of a simple material removal process corrected using the proposed approach.
参考中译：工业机器人以其重复性高、工作空间大、成本低等优点被越来越多地应用于大型零件的加工。然而，将工业机器人应用于大型零部件的高精度加工仍然是一个挑战，其中的主要障碍是由于关节灵活性而导致的机械手刚度不足。当机器人执行加工任务时，关节柔性引起的电机和连杆之间的位置误差称为关节位置误差(JPEs)，它是机器人变形的主要来源，使机器人偏离预期路径。对于大多数工业机器人来说，由于缺乏链路端编码器，直接测量JPE并在控制器中进行补偿是困难的，这极大地恶化了机器人在加工过程中的路径精度。针对这一问题，本文提出了一种基于JPE估计和补偿的工业机器人实时路径修正方法，只需要电机侧测量和外部扳手。建议的方法分为三个步骤。首先，为了实时估计机器人的实际连杆位置，引入了具有关节柔性的机械手动力学，称为柔性动力学(FD)。其次，综合考虑故障检测和干扰动态，提出了一种新的链路状态估计器--基于柔性动力学的干扰卡尔曼滤波(FDBDKF)，实现了JPEs的实时估计。第三，提出了一种基于数据驱动局部加权投影回归(LWPR)的JPE预测与补偿方法，进一步提高了JPE的补偿精度。在六自由度工业机器人上的仿真和实验结果证明了该方法的可行性和有效性。实验结果表明，使用该方法修正的简单材料去除过程的路径精度有显著提高(>80%)。



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