Robotic Tool Tracking Under Partially Visible Kinematic Chain: A Unified Approach
Robotic Tool Tracking Under Partially Visible Kinematic Chain: A Unified Approach
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DOI：
10.1109/tro.2021.3111441
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发表时间：
2021-02
影响因子：
7.8
通讯作者：
Florian Richter;Jingpei Lu;Ryan K. Orosco;Michael C. Yip
中科院分区：
文献类型：
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作者：
Florian Richter;Jingpei Lu;Ryan K. Orosco;Michael C. Yip
当机器人机械手通过视觉反馈控制时，机器人和摄像机之间的变换必须是已知的。然而，在摄像机只能捕捉机器人操作器的一部分以便更好地感知与之交互的环境的情况下，对基座到摄像机变换的校准中的误差有更大的敏感性。机器人控制过程中的第二个不确定性来源是关节角度测量的不准确，这可能是由于定位偏差和复杂的传输效应(如间隙和电缆拉伸)造成的。在这项工作中，当运动链在相机视图中部分可见时，我们将这两组未知参数结合到一个统一的问题公式中。我们证明了这些参数是不可辨识的，这意味着对它们的显式估计是不可行的。为了克服这一点，我们推导出一组较小的参数，我们称之为集中误差，因为它将校准和关节角度测量的误差集中在一起。提出了一种粒子滤波方法，并在仿真和两个真实机器人上进行了测试，估计了集中误差，证明了这种参数减少的有效性。
Anytime a robot manipulator is controlled via visual feedback, the transformation between the robot and camera frame must be known. However, in the case where cameras can only capture a portion of the robot manipulator in order to better perceive the environment being interacted with, there is greater sensitivity to errors in calibration of the base-to-camera transform. A secondary source of uncertainty during robotic control are inaccuracies in joint angle measurements which can be caused by biases in positioning and complex transmission effects such as backlash and cable stretch. In this work, we bring together these two sets of unknown parameters into a unified problem formulation when the kinematic chain is partially visible in the camera view. We prove that these parameters are nonidentifiable implying that explicit estimation of them is infeasible. To overcome this, we derive a smaller set of parameters we call lumped error since it lumps together the errors of calibration and joint angle measurements. A particle filter method is presented and tested in simulation and on two real world robots to estimate the lumped error and show the efficiency of this parameter reduction.