協働ロボットのための高速な物体慣性パラメータ同定
Fast Object Inertial Parameter Identification for Collaborative Robots
協働ロボットの低速・低SNR動作でも高精度に物体の慣性パラメータを推定できるよう、剛体力学の近似と形状情報を用いた質量離散化手法を提案し、シミュレーションと実機で有効性を示した。
著者: Philippe Nadeau, Matthew Giamou, Jonathan Kelly
分類: cs.RO
原文アブストラクト
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.
