拘束系ロボットのシステム同定
System Identification For Constrained Robots
閉リンク機構など拘束を持つロボット向けに、反復最小二乗法でモータ慣性や関節摩擦を同定する手法を提案し、ヒューマノイドDigitで有効性を実証した。
著者: Bohao Zhang, Daniel Haugk, Ram Vasudevan
分類: cs.RO
原文アブストラクト
Identifying the parameters of robotic systems, such as motor inertia or joint friction, is critical to satisfactory controller synthesis, model analysis, and observer design. Conventional identification techniques are designed primarily for unconstrained systems, such as robotic manipulators. In contrast, the growing importance of legged robots that feature closed kinematic chains or other constraints, poses challenges to these traditional methods. This paper introduces a system identification approach for constrained systems that relies on iterative least squares to identify motor inertia and joint friction parameters from data. The proposed approach is validated in simulation and in the real-world on Digit, which is a 20 degree-of-freedom humanoid robot built by Agility Robotics. In these experiments, the parameters identified by the proposed method enable a model-based controller to achieve better tracking performance than when it uses the default parameters provided by the manufacturer. The implementation of the approach is available at https://github.com/roahmlab/ConstrainedSysID.
