An efficient robot payload identification method for industrial application

Dong, Yunfei; Ren, Tianyu; Chen, Ken; Wu, Dingrong

doi:10.1108/ir-03-2018-0037

Cited by 23 publications

(10 citation statements)

References 29 publications

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“…In the practical work of a robot, the relative and absolute values of the Coriolis and centripetal moments are sufficiently small to be neglected when the joints move at low speed (Dong et al, 2018). Besides, the torque caused by gravity is approximately 10s to 100s of times larger than that caused by even the maximum angular acceleration , which implies the estimation of inertia tensor has little effect on the driving torque.…”

Section: Gravity Equationmentioning

confidence: 99%

“…Atkeson et al (1986) adopted the leastsquares (LS) method to realize the estimation of rigid-body dynamic parameters of a manipulator. Dong et al (2018) designed excitation trajectories according to the direct dynamic identification model and applied the LS method for identifying the parameters of the robot payload. Bingül and Karahan (2011) used the LS method and the particle swarm optimization (PSO) technique to estimate distinct inertia parameters of a robot.…”

Section: Introductionmentioning

confidence: 99%

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Estimation of gravity parameters for multi-DOF serial manipulator arms using model learning with sparsity inducing norms

Yang

et al. 2021

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Purpose This study aims to propose a novel method based on model learning with sparsity inducing norms for estimating dynamic gravity terms of the serial manipulators. This method is realized by operating the robot, acquiring data and filtering the features in signal acquisition to adapt to the dynamic gravity parameters. Design/methodology/approach The core principle of the method is to analyze the dictionary composition of the basis function of the model based on the dynamic equation and the Jacobian matrix of an arm. According to the structure of the basis function and the sparsity of the features, combined with joint-angle and driving-torque data acquisition, the effective features of dynamic gravity parameters are screened out using L1-norm optimization and learning algorithms. Findings The theoretical analysis revealed that training data obtained based on joint angles and driving torques could rapidly update dynamic gravity parameters. The simulation experiment was carried out by using the publicly available robot model and compared with the previous disassembly method to evaluate the feasibility and performance. The real 7-degree of freedom (DOF) industrial manipulator was used to further discuss the effects of the feature selection. The results show that this estimation method can be fully operational and efficient in industrial applications. Research limitations/implications This approach is applicable to most serial robots with multi-DOF and the dynamic gravity parameters of the robot are estimated through learning and optimization. The method does not require prior knowledge of the robot arm structure and only requires joint-angle and driving-torque data acquisition under low-speed motion. Furthermore, as it is a data-driven-based method, it can be applied to gravity parameters updating. Originality/value Different from previous general robot dynamic modelling methods, the sparsity of the analytical form of dynamic equations was exploited and model learning was formulated as a convex optimization problem to achieve effective gravity parameters screening. The novelty of this estimation approach is that the method does not only require any prior knowledge but also does not require a specifically designed trajectory. Thus, this method can avoid the laborious work of parameter calibration and the induced modelling errors. By using a data-driven learning approach, the new parameter updating process can be completed conveniently when the robot carries additional mass or the end-effector changes for different tasks.

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Section: Gravity Equationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Estimation of gravity parameters for multi-DOF serial manipulator arms using model learning with sparsity inducing norms

Yang

et al. 2021

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“…In [10], the dynamics of the payload were determined by an identification process that measured actuator currents. In this method, the dynamic parameters of the manipulator were known.…”

Section: Introductionmentioning

confidence: 99%

Dynamic parameter identification method for robotic arms with static friction modelling

Szabó

Szadeczky-Kardoss

2021

ACTA IMEKO

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This paper presents an identification method for robotic manipulators. It demonstrates how a dynamic model can be constructed with the help of the modified Newton–Euler formula. To model the friction of the joints, static friction modelling is used, in which the friction behaviour depends only on the actual velocity of the given joint. With these techniques, the model can be converted into a linear-in-parameters form, which can make the identification process easier. Two estimators are introduced to solve the identification problem, the least-squares and the weighted least-squares estimators, and the determination of the independently identifiable parameter vector to make the regression matrix maximal column rank is presented. The Frobenius norm is used as the condition of the regression matrix to optimise the excitation trajectories, and the form of the trajectories has been selected from the finite Fourier series. The method is tested in a simulated environment to achieve a three-degrees-of-freedom manipulator.

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“…Many researchers studied the positioning repeat-ability and payload identification of industrial robots. 3,4 Li and Du modelled processing lines for sheet metals through the simulation software, thus improving production efficiency. 5 Yan et al developed a robot for sheet metal bending in a five-axis rectangular coordinate system and mainly analysed the flexural rigidity of the main structure.…”

Section: Introductionmentioning

confidence: 99%

Structural model and dynamic analysis of six-axis Cartesian coordinate robot for sheet metal bending

Jiang

Rong

et al. 2019

International Journal of Advanced Robotic Systems

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Bending is an important procedure for processing sheet metals, while it is a key link in the realization of automatic processing of sheet metal. To improve the efficiency and accuracy of bending processing, this article proposed a structure model and a prototype of a six-axis Cartesian coordinate robot for sheet metal bending to replace workers completing automatic bending processes. Based on the analysis of overall structure schemes of the robot, kinematic simulation is conducted by using the automatic dynamic analysis of mechanical system (ADAMS). Furthermore, the dynamic performance of the structural model of the robot for sheet metal bending is analysed and design optimization is performed. A prototype of the robot based on the optimal structural model of six-axis Cartesian coordinate robot for sheet metal bending is made. Finally, under the work conditions, the efficiencies and accuracies of sheet metal bending by a worker and the robot are compared and tested. The structural model of six-axis Cartesian coordinate robot for sheet metal bending presented in this article is found to be applicable to sheet metal bending robot and improves the stability of sheet metal bending machine. The laboratory testing and experimental results verified the feasibility of the proposed robot.

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An efficient robot payload identification method for industrial application

Cited by 23 publications

References 29 publications

Estimation of gravity parameters for multi-DOF serial manipulator arms using model learning with sparsity inducing norms

Estimation of gravity parameters for multi-DOF serial manipulator arms using model learning with sparsity inducing norms

Dynamic parameter identification method for robotic arms with static friction modelling

Structural model and dynamic analysis of six-axis Cartesian coordinate robot for sheet metal bending

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