论文标题
合作机器人的快速对象惯性参数识别
Fast Object Inertial Parameter Identification for Collaborative Robots
论文作者
论文摘要
协作机器人（COBOTS）是旨在与以人为中心环境并肩作用的机器。提供可快速推断受操纵物体的惯性参数的能力的能力将提高其灵活性，并在制造和其他区域中使用更多的使用。为了确保安全性，玉米饼受到运动限制的约束，导致速度，加速度和力刺激数据的信噪比（SNR）较低。这使现有的惯性参数识别算法过慢且不准确。由于渴望更快的模型获取的渴望，我们研究了刚体动力学的近似值来改善SNR。此外，我们引入了一种质量离散方法，该方法可以利用形状信息快速识别操纵对象的合理惯性参数。我们提出了广泛的仿真研究和现实世界实验，表明我们的方法通过专门针对典型的柯比特操作制度来补充现有的惯性参数识别方法。
Collaborative robots (cobots) are machines designed to work safely alongside people in human-centric environments. Providing cobots with the ability to quickly infer the inertial parameters of manipulated objects will improve their flexibility and enable greater usage in manufacturing and other areas. To ensure safety, cobots are subject to kinematic limits that result in low signal-to-noise ratios (SNR) for velocity, acceleration, and force-torque data. This renders existing inertial parameter identification algorithms prohibitively slow and inaccurate. Motivated by the desire for faster model acquisition, we investigate the use of an approximation of rigid body dynamics to improve the SNR. Additionally, we introduce a mass discretization method that can make use of shape information to quickly identify plausible inertial parameters for a manipulated object. We present extensive simulation studies and real-world experiments demonstrating that our approach complements existing inertial parameter identification methods by specifically targeting the typical cobot operating regime.
