Abstract
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.
Highlights
IntroductionThe growing popularity of adaptive, predictive, and passivity-based control strategies in robotic applications raises new challenges for researchers and engineers
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-Identification Model (IDIM)-OLS, -WLS, -Iteratively Reweighted Least-Squares (IRLS), PC-IDIM-Instrumental Variables (IV), and PC-Dynamic Identification Model (DIDIM)
Model generation is the very first step toward identification: should the robot be simulated in a realistic manner, but its dynamics should be expressed in the form of a system of linear equations
Summary
The growing popularity of adaptive, predictive, and passivity-based control strategies in robotic applications raises new challenges for researchers and engineers Since these methods rely on an explicit formulation of system dynamics, substantial research efforts are being made in the field of inertial parameter identification to improve their performance and robustness, see for example [1] and the references therein. The most common approach to offline or batch dynamic parameter identification is the Inverse Dynamic Identification Model with ordinary Least-Squares estimation (referred to as IDIM-OLS in [2,3]) This method is based on the assumption that the mapping between joint torques and the inertial parameters of a robot is linear (this holds provided that the robot links are rigid, that friction nonlinearity is negligible and that the joints are not subject to backlash). Both efficient and easy to implement, IDIM-OLS and its variants—including
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