A Data-Driven Approach for Online Path Correction of Industrial Robots Using Modified Flexible Dynamics Model and Disturbance State Observer

Lin, Yun Fei; Zhao, Huan; Ding, Han

doi:10.1109/tmech.2023.3267980

IEEE/ASME Trans. Mechatron.

2023

DOI: 10.1109/tmech.2023.3267980

|View full text |Cite

A Data-Driven Approach for Online Path Correction of Industrial Robots Using Modified Flexible Dynamics Model and Disturbance State Observer

Yun Fei Lin

Huan Zhao

Han Ding

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

2025

Publication Types

Select...

Article4

Relationship

Self Cite0

Independent4

Authors

Journals

Cited by 4 publications

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

A two‐stage Bayesian framework for rapid dynamics identification in industrial robots

Qiao,

Yang,

Liu

et al. 2024

Intl J Robust & Nonlinear

View full text Add to dashboard Cite

Accurate dynamic models that update in real‐time are vital for industrial robots to adapt flexibly and robustly to evolving environments and tasks, enabling precise model‐based control, motion planning and disturbance estimation. However, the identification of a dynamic robot model involves multiple challenges. First, existing friction models cannot accurately characterize torque variance during prolonged robot operations. Second, traditional offline identification methods such as the least squares (LS) method, suffer from over‐fitting when the inertia parameters are unknown a priori. Furthermore, existing online identification approaches, such as sliding LS, fail to meet the accuracy and real‐time requirements of industrial robots. To address these challenges, this article proposes a two‐stage Bayesian framework for rapid offline identification and online updating of industrial robot dynamics. In the offline stage, we developed a recursive sparse Bayesian learning (RSBL) method to select the dominant parameters and discover a thermal‐related nonlinear dynamic friction model from data, which was then used to find a sparser and simpler inertia model. The obtained friction and inertia models were used as prior knowledge for online updating. The RSBL method significantly reduces the computational complexity and runtime, while it also accelerates online model updating. Experimental results for a 6‐DOF medium‐load robot validate dual offline improvements in prediction accuracy and model sparsity, along with higher online prediction accuracy and shorter computational time.

show abstract

A two‐stage Bayesian framework for rapid dynamics identification in industrial robots

Qiao,

Yang,

Liu

et al. 2024

Intl J Robust & Nonlinear

View full text Add to dashboard Cite

show abstract

Tool pose measurement using shadow imaging sensors

Terlau,

von Freyberg,

Fischer

2024

Int J Adv Manuf Technol

View full text Add to dashboard Cite

Due to the high positioning uncertainty and low stiffness of robots, significant geometrical deviations occur in robotic incremental sheet forming (ISF) which requires an optical in-process tool pose measurement. To achieve a measuring uncertainty of less than 50 $$\upmu $$ μ m for the position components and less than 0.05$$^\circ $$ ∘ for the orientation components in the machining volume, a multi-sensor system based on shadow imaging sensors is proposed. To calculate the six-degree-of-freedom tool pose, the sensor system measures the positions of three LEDs attached to the tool. The LEDs emit light with different colors, and the associated shadows are separately detected by using color filters. In addition, each sensor consists of a shadow-casting mask and a monochrome camera. Experimental results show that the systematic error dominates the measurement uncertainty budget and needs to be calibrated, while the random error is one order of magnitude smaller than the required measuring uncertainty. Furthermore, the color filters reduce the cross-sensitivities between the signals to an acceptable level. Final experiments on a robot demonstrate plausible pose measurements indicating the feasibility to apply the multi-sensor system in robotic ISF.

show abstract

Analysis for time varying torsional stiffness of RV reducer

Li,

Zhang,

Gao

2025

Mechanical Systems and Signal Processing

View full text Add to dashboard Cite

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

A Data-Driven Approach for Online Path Correction of Industrial Robots Using Modified Flexible Dynamics Model and Disturbance State Observer

Cited by 4 publications

References 31 publications

A two‐stage Bayesian framework for rapid dynamics identification in industrial robots

A two‐stage Bayesian framework for rapid dynamics identification in industrial robots

Tool pose measurement using shadow imaging sensors

Analysis for time varying torsional stiffness of RV reducer

Contact Info

Product

Resources

About