Dynamic Parameter Identification of a Curtain Wall Installation Robotic Arm

2025-07-22

AI Summary

To address the low efficiency and insufficient intelligence of conventional methods in building façade installation, this paper proposes a high-precision dynamic parameter identification method tailored for hydraulic-driven façade-installation robotic arms. The method innovatively formulates a D-H parametrized system integrating hydraulic cylinder dynamics with the Stribeck friction model. A hierarchical progressive identification strategy combined with an optimal Fourier-based excitation trajectory is designed to achieve decoupled and joint calibration of dynamic parameters for both the hydraulic cylinders and robot joints—while simultaneously identifying the Stribeck friction characteristics at each joint—under high signal-to-noise-ratio displacement excitation. Experimental results demonstrate that the joint torque residual standard deviation is reduced to below 0.4 Nm, significantly enhancing modeling accuracy and operational intelligence of hydraulic-driven façade-installation robots.

Technology Category

• Intelligent Robots: State Estimation
• Computer Vision: Low Level & Physics-based Vision
• Planning, Routing, and Scheduling: Activity and Plan Recognition

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

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.

Problem

• Identifies dynamic parameters of hydraulic robotic arm for curtain walls
• Develops excitation method to optimize parameter identification accuracy
• Validates high-precision modeling for construction efficiency and quality

Innovation

• Hydraulically driven robotic arm for curtain wall installation
• Dynamic parameter identification with Stribeck friction model
• Hierarchical progressive parameter identification strategy