Constrained dynamic parameter estimation using the Extended Kalman Filter

Joukov, Vladimir; Bonnet, Vincent; Venture, Gentiane; Kulić, Dana

doi:10.1109/iros.2015.7353888

Cited by 11 publications

(11 citation statements)

References 13 publications

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“…As explained in [77], identification can be carried out in two different manners, denoted respectively as dual method (c.f. [78,79]) and joint method (c.f. [7,14,80,81]).…”

Section: Direct Dynamics Identification Model (Ddim) and Nonlinear Kalman Filtering (Nkf)mentioning

confidence: 99%

“…In the first approach, the system state and parameters are identified separately, within two concurrent Kalman filter instances, while in the second one, state and parameters are estimated simultaneously, within a single Kalman filter featuring an augmented state representation. With the notable exception of [79], approaches to robot dynamic parameters identification found in the scientific literature are usually based on the joint filtering paradigm. Since this approach allows accounting for the statistical coupling between the state and the parameters, as suggested by [82], it is, therefore, expected to be significantly more robust than the dual filtering method.…”

Section: Direct Dynamics Identification Model (Ddim) and Nonlinear Kalman Filtering (Nkf)mentioning

confidence: 99%

“…In [79], the authors used a different state representation, assuming that the noise level in the joint encoders was negligible, and that the joint motion derivatives could therefore be computed independently. In this context, the state update Equation ( 48) can be simplified into Γ(z k ) =β k KF , with z k =β k−1 KF while the measurement prediction equation consists of the IDM, computed using the Recursive Newton Euler (RNE) algorithm (c.f.…”

Section: Direct Dynamics Identification Model (Ddim) and Nonlinear Kalman Filtering (Nkf)mentioning

confidence: 99%

“…[83]). Note that this is not the exact formulation of [79], as the authors use sigmoid barrier functions to enforce physical consistency. This aspect is discussed in greater details in Section 7.…”

Section: Direct Dynamics Identification Model (Ddim) and Nonlinear Kalman Filtering (Nkf)mentioning

confidence: 99%

“…In this context, ref. [16] proposed a reformulation of IDIM-OLS as a nonlinearly constrained optimization problem, solved using Sequential Quadratic Programming (SQP), while [79] suggested to directly identify the elements ofȊ j instead of L j , using sigmoid functions within an EKF identification loop to smoothly bound the parameter estimates to physicality. It is worth pointing that such an approach could also possibly be applied to the HTRNN identification process by modifying the hyperbolic tangent activation function, although, to the best of our knowledge, this was never done.…”

Section: Enforcing Physical Consistency Within Inertial Parameter Identification 71 Mathematical Formulation Of the Physical Consistency mentioning

confidence: 99%

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Inertial Parameter Identification in Robotics: A Survey

et al. 2021

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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.

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“…As explained in [77], identification can be carried out in two different manners, denoted respectively as dual method (c.f. [78,79]) and joint method (c.f. [7,14,80,81]).…”

Section: Direct Dynamics Identification Model (Ddim) and Nonlinear Kalman Filtering (Nkf)mentioning

confidence: 99%

Section: Direct Dynamics Identification Model (Ddim) and Nonlinear Kalman Filtering (Nkf)mentioning

confidence: 99%

Section: Direct Dynamics Identification Model (Ddim) and Nonlinear Kalman Filtering (Nkf)mentioning

confidence: 99%

Section: Direct Dynamics Identification Model (Ddim) and Nonlinear Kalman Filtering (Nkf)mentioning

confidence: 99%

Section: Enforcing Physical Consistency Within Inertial Parameter Identification 71 Mathematical Formulation Of the Physical Consistency mentioning

confidence: 99%

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