Optimal design for compliance modeling of industrial robots with bayesian inference of stiffnesses

Tepper, Cornelia; Matei, Alexander; Zarges, Jonas; Ulbrich, Stefan; Weigold, Matthias

doi:10.1007/s11740-023-01198-3

Cited by 3 publications

(3 citation statements)

References 22 publications

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“…And the parameters of the robot link self-weight compliance model are expressed as η Link = s * 1 , s * 2 . In summary, combining Equations ( 7), ( 17), (24), and (26), the kinematic and joint compliance model can be expressed as Equation (27) in differential form.…”

Section: Compliance Error Caused By Weight Of Robot Linkmentioning

confidence: 99%

“…. In summary, combining Equations ( 7), ( 17), (24), and (26), the kinematic and joint compliance model can be expressed as Equation (27) in differential form.…”

Section: Torque Link1 Link2mentioning

confidence: 99%

“…Utilizing single joint rotations and laser tracking measurements, the study identifies significant compliance errors and compensated for them, markedly improving robot accuracy and operational efficiency [ 23 ]. Tepper proposed a cost- and time-efficient approach for setting up a compliance model for industrial robots, utilizing an optimal design of experiments for variance-minimal Bayesian inference of gear stiffness parameters [ 24 ].…”

Section: Introductionmentioning

confidence: 99%

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Kinematic and Joint Compliance Modeling Method to Improve Position Accuracy of a Robotic Vision System

Ye,

Jia,

Wang

et al. 2024

Sensors

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In the field of robotic automation, achieving high position accuracy in robotic vision systems (RVSs) is a pivotal challenge that directly impacts the efficiency and effectiveness of industrial applications. This study introduces a comprehensive modeling approach that integrates kinematic and joint compliance factors to significantly enhance the position accuracy of a system. In the first place, we develop a unified kinematic model that effectively reduces the complexity and error accumulation associated with the calibration of robotic systems. At the heart of our approach is the formulation of a joint compliance model that meticulously accounts for the intricacies of the joint connector, the external load, and the self-weight of robotic links. By employing a novel 3D rotary laser sensor for precise error measurement and model calibration, our method offers a streamlined and efficient solution for the accurate integration of vision systems into robotic operations. The efficacy of our proposed models is validated through experiments conducted on a FANUC LR Mate 200iD robot, showcasing notable improvements in the position accuracy of robotic vision system. Our findings contribute a framework for the calibration and error compensation of RVS, holding significant potential for advancements in automated tasks requiring high precision.

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Section: Compliance Error Caused By Weight Of Robot Linkmentioning

confidence: 99%

“…. In summary, combining Equations ( 7), ( 17), (24), and (26), the kinematic and joint compliance model can be expressed as Equation (27) in differential form.…”

Section: Torque Link1 Link2mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Kinematic and Joint Compliance Modeling Method to Improve Position Accuracy of a Robotic Vision System

Ye,

Jia,

Wang

et al. 2024

Sensors

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Using statistical linearization in experiment design for identification of robotic manipulators

Zimmermann,

Moberg,

Gunnarsson

et al. 2024

Control Engineering Practice

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On the Optimization of Robot Machining: A Simulation-Based Process Planning Approach

Souflas,

Gerontas,

Bikas

et al. 2024

Machines

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The use of industrial robots for machining operations is pursued by industry lately, since they can increase the flexibility of the production system and reduce production costs. However, their industrial adoption is still limited, mainly due to their insufficient structural stiffness and posture-dependent dynamic behavior, leading to limited machining process accuracy. For this purpose, the Digital-Model of a machining robot has been developed, providing a tool for virtual commissioning of the process that can be used during the process planning stage. The Multi-Body Simulation method combined with a Component Mode Synthesis have been adopted, considering flexibility of both the joints and links. On top of that, and motivated from robotic-based machining systems’ flexibility and versatility, two optimization algorithms have been developed, attempting to increase the process accuracy. A workpiece placement optimization algorithm, attempting to maximize the robot stiffness during the process acquiring knowledge from the robot stiffness maps, and a feed-rate scheduling algorithm, attempting to constrain the contour error by regulating the generated cutting forces. The capabilities and functionality of the developed model and optimization algorithms are showcased in two different case studies, with the results proving the improvements on the process accuracy after the application of the optimization algorithms. Finally, an experimental validation of the Digital-Model has been performed, to confirm the consistency between model outputs and real experimental data.

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Optimal design for compliance modeling of industrial robots with bayesian inference of stiffnesses

Cited by 3 publications

References 22 publications

Kinematic and Joint Compliance Modeling Method to Improve Position Accuracy of a Robotic Vision System

Kinematic and Joint Compliance Modeling Method to Improve Position Accuracy of a Robotic Vision System

Using statistical linearization in experiment design for identification of robotic manipulators

On the Optimization of Robot Machining: A Simulation-Based Process Planning Approach

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