基于SVA的瓦尔基里人形机器人系统辨识与控制
System Identification and Control of Valkyrie through SVA--Based Regressor Computation
AI总结：
本文利用空间向量代数(SVA)实现瓦尔基里人形机器人的同时辨识与控制，通过空间惯性张量计算动力学参数，提出高效算法实现回归器评估，用于在线系统辨识与基于模型的控制。
摘要：
本文演示了利用空间向量代数(SVA)对人形机器人Valkyrie进行的同时辨识与控制。特别是，通过空间惯性张量计算机器人动力学中的惯性、科里奥利-离心力和重力项。在假设链接长度或关节轴间距离准确已知的情况下，将证明机器人惯性属性可直接从惯量张量评估。提出了一种算法来评估回归器，其运行时间为O(n²)。该算法的效率为通过基于SVA的回归器实现在线系统辨识提供了手段，并作为副产品，提供了一种精确的基于模型的控制方法。通过在三个案例研究中的实现，提供了所提方法的实验验证：离线识别双摆和4自由度机械腿，以及在线识别和控制4自由度机械臂。
英文摘要：
This paper demonstrates simultaneous identification and control of the humanoid robot, Valkyrie, utilizing Spatial Vector Algebra (SVA). In particular, the inertia, Coriolis-centrifugal and gravity terms for the dynamics of a robot are computed using spatial inertia tensors. With the assumption that the link lengths or the distance between the joint axes are accurately known, it will be shown that inertial properties of a robot can be directly evaluated from the inertia tensor. An algorithm is proposed to evaluate the regressor, yielding a run time of $O(n^2)$. The efficiency of this algorithm yields a means for online system identification via the SVA--based regressor and, as a byproduct, a method for accurate model-based control. Experimental validation of the proposed method is provided through its implementation in three case studies: offline identification of a double pendulum and a $4$-DOF robotic leg, and online identification and control of a $4$-DOF robotic arm.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
