Fast Object Inertial Parameter Identification for Collaborative Robots

TL;DR

This paper introduces a fast inertial parameter identification method for collaborative robots that leverages rigid body dynamics approximation and mass discretization to improve speed and accuracy in low SNR conditions.

Contribution

The paper proposes a novel approach combining dynamics approximation and shape-based mass discretization for rapid inertial parameter estimation in cobots.

Findings

Significantly faster parameter identification in simulations and real-world tests.

Improved accuracy under low SNR conditions compared to existing methods.

Effective use of shape information to narrow down plausible inertial parameters.

Abstract

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.