Sequential semidefinite optimization for physically and statistically consistent robot identification

Janot, Alexandre; Wensing, Patrick M.

doi:10.1016/j.conengprac.2020.104699

Cited by 8 publications

(6 citation statements)

References 48 publications

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“…Please note that this is approximately five times higher than IDIM-OLS, which can be explained by the process of DDM integration occurring during the simulation step within DIDIM and IDIM-IV. As expected, physically consistent identification methods, based on semidefined programming algorithms, take about three times longer than their unconstrained counterparts, which is consistent with the performance of current SDP solvers (in our case CVX with MOSEK) and aligned with the results of [92]. It is worth noting that the number of iterations-and hence model recalculation-required by PC-IDIM-IV and PC-DIDIM are similar, in the case of proper convergence, to that of the unconstrained IDIM-IV and DIDIM, respectively.…”

Section: Convergence and Computational Complexitysupporting

confidence: 87%

“…[23]), IDIM-IV and DIDIM (c.f. [92]) also makes it possible to seamlessly integrate the LMI physicality constraints by actually solving an SDP at each iteration of the corresponding algorithms. For instance, the PC-DIDIM algorithm (proposed in [92]) can be implemented by solving at each iteration î…”

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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Section: Convergence and Computational Complexitysupporting

confidence: 87%

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

confidence: 99%

Inertial Parameter Identification in Robotics: A Survey

et al. 2021

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“…To derive the equation of the unsprung mass angular momentum, similar to e equation of the sprung mass angular momentum (15), Equations ( 19)-( 21) are obtained as follows: , (28) where n shows the unsprung masses. In Equations ( 28) and (29) Z n is the displacement of a point of the sprung mass in the vertical direction above the unsprung masses, which is calculated according to Equation (30): The left-hand side of Equations ( 13) and ( 25)- (27), which are the main equations of motion of the vehicle, includes the forces and torques of the external forces applied to the vehicle and an example of them is shown in Figures 2 and 3. Note that by considering the directions of the coordinate systems, these forces and torques are entered into the equations of motion of the vehicle.…”

Section: Modeling and Equationsmentioning

confidence: 99%

“…Huang et al [24] proposed a method for dynamic balance measurement and imbalance compensation of crankshaft assemblies. Moreover, basic researches of dynamic modeling have been reported in [25][26][27][28] This study, by presenting a 15-DOF model of the vehicle dynamics, considers reducing the complexity of the model to the extent that it would be acceptable for the dynamic behavior of the vehicle studying and modeling the necessary subsystems, such as tire and engine, with sufficient accuracy. The tire is modeled with the Pacejka 89 model, which calculates the tire forces using the longitudinal and lateral slips.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Behavior of the Full-Car Model in the J-Turn Maneuver by Considering the Engine Gyroscopic Effects

Shahabi¹,

Kazemian²,

Farahat³

et al. 2021

Komunikácie

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This study presents a new dynamic modeling of a vehicle by considering the engine dynamics. By selecting the vehicle coordinate system as the reference frame, all the force-torque equations of the sprung mass and unsprung masses are derived in this coordinate system by using the Newton’s equations of motion. Unlike the previous researches, in this work the sprung mass of the vehicle is not considered as a rigid body. The dynamics of the sprung mass components, such as gyroscopic effects of the engine crankshaft, is considered. In order to study the vehicle's dynamic behavior, in the J-turn maneuver, the governing equations of the full-car model are evaluated and validated by the numerical simulation method and ADAMS/Car software. Based on the results, the maximum roll angle and roll rate of a vehicle reach about 8 degrees and 40 degrees per second, respectively.

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“…Lee [15] proposed a geometric programming approach based on the Riemannian structure of positive definite matrices, which enables semidefinite optimization for convex regularization of parameter estimates. Then, Janot [16] developed a sequential semidefinite optimization procedure for ensuring the physical and statistical consistency of the identified model.…”

Section: Introductionmentioning

confidence: 99%

An improved iterative approach with a comprehensive friction model for identifying dynamic parameters of collaborative robots

Li,

Wei,

Liu

et al. 2024

Robotica

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Collaborative robots are becoming intelligent assistants of human in industrial settings and daily lives. Dynamic model identification is an active topic for collaborative robots because it can provide effective ways to achieve precise control, fast collision detection and smooth lead-through programming. In this research, an improved iterative approach with a comprehensive friction model for dynamic model identification is proposed for collaborative robots when the joint velocity, temperature and load torque effects are considered. Experiments are conducted on the AUBO I5 collaborative robots. Two other existing identification algorithms are adopted to make comparison with the proposed approach. It is verified that the average error of the proposed I-IRLS algorithm is reduced by over 14% than that of the classical IRLS algorithm. The proposed I-IRLS method can be widely used in various application scenarios of collaborative robots.

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Sequential semidefinite optimization for physically and statistically consistent robot identification

Cited by 8 publications

References 48 publications

Inertial Parameter Identification in Robotics: A Survey

Inertial Parameter Identification in Robotics: A Survey

Dynamic Behavior of the Full-Car Model in the J-Turn Maneuver by Considering the Engine Gyroscopic Effects

An improved iterative approach with a comprehensive friction model for identifying dynamic parameters of collaborative robots

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