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.

翻译：协作机器人（cobot）是一种设计用于在人本环境中与人类安全共存的机器。赋予协作机器人快速推断操作物体惯性参数的能力，将提升其灵活性，并促进其在制造业及其他领域的广泛应用。为确保安全性，协作机器人受运动学限制，导致速度、加速度及力-力矩数据的信噪比较低。这使得现有的惯性参数辨识算法变得极其缓慢且不精确。基于对更快模型获取的需求，我们探索利用刚体动力学近似方法来提升信噪比。此外，我们提出一种质量离散化方法，可借助形状信息快速识别操作物体的合理惯性参数。通过大量仿真研究与实际实验证明，我们的方法通过针对性优化典型协作机器人运行状态，有效补充了现有惯性参数辨识技术。