Comparison of trajectory parametrization methods with statistical analysis for dynamic parameter identification of serial robot

Abu‐Dakka, Fares J.; Díaz‐Rodríguez, Miguel

doi:10.1109/iros.2017.8206479

Cited by 3 publications

(4 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This can be achieved by means of specific joint trajectories, referred to as exciting. The design of excitation trajectories has been widely investigated over the past two decades, see e.g., [9,17,23,38,53,[99][100][101][102]. From these works, it appears that the conditioning of the observation matrix W is a relevant quality indicator for the generated trajectory.…”

Section: Trajectory Data Generationmentioning

confidence: 99%

“…From these works, it appears that the conditioning of the observation matrix W is a relevant quality indicator for the generated trajectory. In our case, joint trajectories are obtained by parametrization of finite Fourier series as presented in [9,23,101]. The following cost criterion J t is minimized over the experiment time horizon using a standard nonlinear programming solver (in our case the "fmincon" and "ga" Matlab functions):…”

Section: Trajectory Data Generationmentioning

confidence: 99%

See 1 more Smart Citation

Inertial Parameter Identification in Robotics: A Survey

et al. 2021

View full text Add to dashboard Cite

This work aims at reviewing, analyzing and comparing a range of state-of-the-art approaches to inertial parameter identification in the context of robotics. We introduce “BIRDy (Benchmark for Identification of Robot Dynamics)”, an open-source Matlab toolbox, allowing a systematic and formal performance assessment of the considered identification algorithms on either simulated or real serial robot manipulators. Seventeen of the most widely used approaches found in the scientific literature are implemented and compared to each other, namely: the Inverse Dynamic Identification Model with Ordinary, Weighted, Iteratively Reweighted and Total Least-Squares (IDIM-OLS, -WLS, -IRLS, -TLS); the Instrumental Variables method (IDIM-IV), the Maximum Likelihood (ML) method; the Direct and Inverse Dynamic Identification Model approach (DIDIM); the Closed-Loop Output Error (CLOE) method; the Closed-Loop Input Error (CLIE) method; the Direct Dynamic Identification Model with Nonlinear Kalman Filtering (DDIM-NKF), the Adaline Neural Network (AdaNN), the Hopfield-Tank Recurrent Neural Network (HTRNN) and eventually a set of Physically Consistent (PC-) methods allowing the enforcement of parameter physicality using Semi-Definite Programming, namely the PC-IDIM-OLS, -WLS, -IRLS, PC-IDIM-IV, and PC-DIDIM. BIRDy is robot-agnostic and features a complete inertial parameter identification pipeline, from the generation of symbolic kinematic and dynamic models to the identification process itself. This includes functionalities for excitation trajectory computation as well as the collection and pre-processing of experiment data. In this work, the proposed methods are first evaluated in simulation, following a Monte Carlo scheme on models of the 6-DoF TX40 and RV2SQ industrial manipulators, before being tested on the real robot platforms. The robustness, precision, computational efficiency and context of application the different methods are investigated and discussed.

show abstract

Section: Trajectory Data Generationmentioning

confidence: 99%

Section: Trajectory Data Generationmentioning

confidence: 99%

Inertial Parameter Identification in Robotics: A Survey

et al. 2021

View full text Add to dashboard Cite

show abstract

“…One of the methods most used is the Inverse Dynamic Identification Model + Least Squares (LS) (IDIM + LS) scheme. 6 –11 An inverse dynamic model that is linear with respect to the dynamic parameters is established. Then, the excitation trajectory is designed with various methods.…”

Section: Introductionmentioning

confidence: 99%

“…Then, the excitation trajectory is designed with various methods. 6,9 The robot input and output signals are sampled during the dynamic excitation experiment. Finally, the parameters are estimated by the LS.…”

Section: Introductionmentioning

confidence: 99%

A dynamic parameter identification method of industrial robots considering joint elasticity

Zhang

et al. 2019

International Journal of Advanced Robotic Systems

View full text Add to dashboard Cite

Considering the joint elasticity, a novel dynamic parameter identification method is proposed for general industrial robots only with motor encoders. Firstly, the unknown parameters of the elastic joint dynamic model are analyzed and divided into two types. The first type is the motion-independent parameter only including the joint stiffness, which can be identified by the static force/torque-deformation experiments without the dynamic model. The second type is the motiondependent parameter composed of the rest of the parameters, which needs the dynamic excitation experiments. Therefore, these two types of parameters can be identified separately. Meanwhile, it is found that the rotor inertia parameters can be obtained from the manufacturer, which reduces the identification difficulty of other parameters. After obtaining the rotor inertia and joint stiffness, an approximate processing algorithm is proposed considering the motor friction to establish the linear identification model of other parameters. Hence, the least squares can be employed to identify the parameters, and the independence of the inertia and joint viscous friction parameters are not affected. Meanwhile, the exciting trajectories can be optimized throughout the robot workspace, which reduces the effect of measurement noise on identification accuracy. With the proposed separated identification strategy and approximate processing algorithm, the dynamic parameters can be obtained precisely without double encoders on each joint. Finally, a series of simulations are conducted to evaluate the good performance of the proposed method.

show abstract

Dynamic modeling and analysis for a differential modular robot joint with the friction model

Liu

Luo

et al. 2022

2022 IEEE International Conference on Robotics and Biomimetics (ROBIO)

View full text Add to dashboard Cite

Comparison of trajectory parametrization methods with statistical analysis for dynamic parameter identification of serial robot

Cited by 3 publications

References 21 publications

Inertial Parameter Identification in Robotics: A Survey

Inertial Parameter Identification in Robotics: A Survey

A dynamic parameter identification method of industrial robots considering joint elasticity

Dynamic modeling and analysis for a differential modular robot joint with the friction model

Contact Info

Product

Resources

About