A dynamic parameter identification method of industrial robots considering joint elasticity

Ni, Hepeng; Zhang, Chengrui; Hu, Tianliang; Wang, Teng; Chen, Qizhi; Chen, Chao

doi:10.1177/1729881418825217

Cited by 18 publications

(12 citation statements)

References 34 publications

(54 reference statements)

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“…Static tests are widely used to obtain the joint stiffness of industrial robots [17]. In the static tests, a set of forces is applied to the robot end-effector in different robot configurations, while the displacement sensors (laser sensors, vision systems, coordinate measuring machines) measure the static deformation of the end-effector.…”

Section: Introductionmentioning

confidence: 99%

Modeling and Identification of an Industrial Robot with a Selective Modal Approach

et al. 2020

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The stiffness properties of industrial robots are very important for many industrial applications, such as automatic robotic assembly and material removal processes (e.g., machining and deburring). On the one hand, in robotic assembly, joint compliance can be useful for compensating dimensional errors in the parts to be assembled; on the other hand, in material removal processes, a high Cartesian stiffness of the end-effector is required. Moreover, low frequency chatter vibrations can be induced when low-stiffness robots are used, with an impairment in the quality of the machined surface. In this paper, a compliant joint dynamic model of an industrial robot has been developed, in which joint stiffness has been experimentally identified using a modal approach. First, a novel method to select the test configurations has been developed, so that in each configuration the mode of vibration that chiefly involves only one joint is excited. Then, experimental tests are carried out in the selected configurations in order to identify joint stiffness. Finally, the developed dynamic model of the robot is used to predict the variation of the natural frequencies in the workspace.

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Section: Introductionmentioning

confidence: 99%

Modeling and Identification of an Industrial Robot with a Selective Modal Approach

et al. 2020

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“…Generally, non-structural uncertainty is the main factor that causes inaccurate identification parameters. Zhang et al considered the joint stiffness into the dynamic model and improved the accuracy of identification [6]. The noise disturbs joint angles and currents, and the optimization of the excitation trajectory can reduce the impact of measurement noise on identification accuracy.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Aided Dynamic Parameter Identification of 6-DOF Robot Manipulators

et al. 2020

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Generally, structural uncertainty of the robot dynamics system refers to model error caused by parameter identification, unstructured uncertainty is the unmodeled dynamic characteristic. No matter how elaborate modeling methods are used, there always be uncertainty. Therefore, this paper applies deep learning for the first time to aid robot dynamic parameter identification of 6 degrees of freedom robot manipulator for compensation of uncertain factors. Firstly, the relatively accurate prediction of torque is obtained by the physical dynamic model using the parameters identification method (errors are less than 10% of the maximum torques). Secondly, we propose a novel deep neural network based approach called Uncertainty Compensation Model (UCM) to compensate the torque error introduced by the uncertainty. The UCM mainly composed by proposed Input Control Module (ICM) and Error Learning Models (ELMs) based on Long-Short-Term Memory and attention mechanism. The proposed ICM, which effectively avoids the unnecessary interference, is used to control valid input for ELMs. The ELMs, consisted by ELM units, concern extracting salient features from sequence data to predict the joint error. Also, this paper summarizes the effects of valid input, timestep and attention mechanism on the performance of the UCM. Finally, the verification of parameter identification and torques compensation is carried out by a Universal Robot 5 manipulator. Compared with the prediction torques of physical dynamic model, the proposed UCM has a good ability to capture the friction characteristics and compensate for the error of local maximum torques, which effectively solves the deficiency of the physical dynamics model and improves prediction accuracy (errors are less than 6% of the maximum torques).

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“…On offline methods, examples can be [18][19][20]. Multiple techniques can be applied through the use of neural networks, as explained in [21] or even experimentally, as the authors of [22,23] present.…”

Section: Introductionmentioning

confidence: 99%

Modeling and Control of a DC Motor Coupled to a Non-Rigid Joint

Pinto

Gonçalves

Costa

2020

ASI

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Throughout this paper, the model, its parameter estimation and a controller for a solution using a DC motor with a gearbox worm, coupled to a non-rigid joint, will be presented. First, the modeling of a non-linear system based on a DC Motor with Worm Gearbox coupled to a non-rigid joint is presented. The full system was modeled based on the modeling of two sub-systems that compose it—a non-rigid joint configuration and the DC motor with the worm gearbox configuration. Despite the subsystems are interdependent, its modelling can be performed independently trough a carefully chosen set of experiments. Modeling accurately the system is crucial in order to simulate and know the expected performance. The estimation process and the proposed experimental setup are presented. This setup collects data from an absolute encoder, a load cell, voltage and current sensors. The data obtained from these sensors is presented and used to obtaining some physical parameters from both systems. Finally, through an optimization process, the remaining parameters are estimated, thus obtaining a realistic model of the complete system. Finally, the controller setup is presented and the results obtained are also presented.

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A dynamic parameter identification method of industrial robots considering joint elasticity

Cited by 18 publications

References 34 publications

Modeling and Identification of an Industrial Robot with a Selective Modal Approach

Modeling and Identification of an Industrial Robot with a Selective Modal Approach

Deep Learning Aided Dynamic Parameter Identification of 6-DOF Robot Manipulators

Modeling and Control of a DC Motor Coupled to a Non-Rigid Joint

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