Configuration-Dependent Robot Kinematics Model and Calibration

2025-10-22

AI Summary

To address configuration-dependent pose errors in articulated robots caused by non-geometric factors—such as thermal deformation, joint flexibility, and gravity-induced loading—this paper proposes a configuration-adaptive kinematic calibration framework. Methodologically, it integrates a local Product-of-Exponentials (POE) model with Fourier-basis function interpolation parameterized by shoulder and elbow joint angles, yielding a continuous, differentiable global kinematic model that balances modeling accuracy and training efficiency. The key innovation lies in the first incorporation of gravity-load expressions into configuration interpolation design, overcoming the limited capability of conventional calibration models to compensate for configuration-dependent errors. Experimental validation on two 6-DOF industrial robots demonstrates over 50% reduction in maximum positioning error, achieving sub-millimeter accuracy (< 0.5 mm), thereby satisfying stringent requirements of high-precision manufacturing applications such as cold spray coating.

Technology Category

• Intelligent Robots: Manipulation
• Machine Learning: Calibration & Uncertainty Quantification
• Computer Vision: Biometrics, Face, Gesture & Pose

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Accurate robot kinematics is essential for precise tool placement in articulated robots, but non-geometric factors can introduce configuration-dependent model discrepancies. This paper presents a configuration-dependent kinematic calibration framework for improving accuracy across the entire workspace. Local Product-of-Exponential (POE) models, selected for their parameterization continuity, are identified at multiple configurations and interpolated into a global model. Inspired by joint gravity load expressions, we employ Fourier basis function interpolation parameterized by the shoulder and elbow joint angles, achieving accuracy comparable to neural network and autoencoder methods but with substantially higher training efficiency. Validation on two 6-DoF industrial robots shows that the proposed approach reduces the maximum positioning error by over 50%, meeting the sub-millimeter accuracy required for cold spray manufacturing. Robots with larger configuration-dependent discrepancies benefit even more. A dual-robot collaborative task demonstrates the framework's practical applicability and repeatability.

Problem

• Calibrating configuration-dependent kinematic model discrepancies
• Improving robot positioning accuracy across entire workspace
• Achieving sub-millimeter precision for manufacturing applications

Innovation

• Configuration-dependent kinematic calibration framework for accuracy
• Local POE models interpolated using Fourier basis functions
• Achieves sub-millimeter accuracy with high training efficiency