Dynamic Parameter Identification of a Curtain Wall Installation Robotic Arm

TL;DR

This paper presents a novel dynamic parameter identification method for a hydraulically driven robotic arm used in curtain wall installation, combining a D-H model, hydraulic dynamics, and optimal excitation signals to achieve high-precision parameter estimation.

Contribution

It introduces a hierarchical identification strategy integrating hydraulic and robotic parameters with optimal excitation trajectories for improved accuracy.

Findings

Residual standard deviation below 0.4 Nm in torque measurements

Effective excitation signals for dynamic parameter identification

High-precision identification validated on experimental platform

Abstract

In the construction industry, traditional methods fail to meet the modern demands for efficiency and quality. The curtain wall installation is a critical component of construction projects. We design a hydraulically driven robotic arm for curtain wall installation and a dynamic parameter identification method. We establish a Denavit-Hartenberg (D-H) model based on measured robotic arm structural parameters and integrate hydraulic cylinder dynamics to construct a composite parametric system driven by a Stribeck friction model. By designing high-signal-to-noise ratio displacement excitation signals for hydraulic cylinders and combining Fourier series to construct optimal excitation trajectories that satisfy joint constraints, this method effectively excites the characteristics of each parameter in the minimal parameter set of the dynamic model of the robotic arm. On this basis, a hierarchical progressive parameter identification strategy is proposed: least squares estimation is employed to separately identify and jointly calibrate the dynamic parameters of both the hydraulic cylinder and the robotic arm, yielding Stribeck model curves for each joint. Experimental validation on a robotic arm platform demonstrates residual standard deviations below 0.4 Nm between theoretical and measured joint torques, confirming high-precision dynamic parameter identification for the hydraulic-driven curtain wall installation robotic arm. This significantly contributes to enhancing the intelligence level of curtain wall installation operations.