Designing G-optimal experiments for robot dynamics identification

Lizama, Edgar Ruiz; Šurdilović, Dragoljub

doi:10.1109/robot.1996.503795

Cited by 10 publications

(4 citation statements)

References 6 publications

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“…All those papers adopt model dependent strategies. Finally we cite [22] for an example of G-optimal experiments for dynamics identification. We exploit the classi-cal theory of DoE and define a methodology for the choice of the paths for efficient gathering and analysis of experimental data during path following experimentations.…”

Section: Related Work and Our Contributionmentioning

confidence: 99%

Adaptive Experimental Design for Path-following Performance Assessment of Unmanned Vehicles

Saggini,

Riccomagno,

Caccia

et al. 2016

Preprint

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The definition of Good Experimental Methodologies (GEMs) in robotics is a topic of widespread interest due also to the increasing employment of robots in everyday civilian life. The present work contributes to the ongoing discussion on GEMs for Unmanned Surface Vehicles (USVs). It focuses on the definition of GEMs and provides specific guidelines for path-following experiments. Statistically designed experiments (DoE) offer a valid basis for developing an empirical model of the system being investigated. A two-step adaptive experimental procedure for evaluating path-following performance and based on DoE, is tested on the simulator of the Charlie USV. The paper argues the necessity of performing extensive simulations prior to the execution of field trials.

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Section: Related Work and Our Contributionmentioning

confidence: 99%

Adaptive Experimental Design for Path-following Performance Assessment of Unmanned Vehicles

Saggini,

Riccomagno,

Caccia

et al. 2016

Preprint

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“…A solid theoretical basis for optimal experiment design is built in [3] and [20], and is widely employed in different areas of engineering. Some examples include sensor array signal processing [8] and robotics [13]. The problem of goal-oriented experiment design has also received a lot of attention, see for example [7], [11], [4] and [6].…”

Section: Introductionmentioning

confidence: 99%

Optimal Excitation for Nonparametric Identification of Viscoelastic Materials

Rensfelt

Söderström

2011

IEEE Trans. Contr. Syst. Technol.

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The problem of optimal excitation in nonparametric identification of viscoelastic materials is considered. The goal is to design the input spectrum in an optimal way, so that the average variance of the estimates is minimized. It is shown how the covariance matrix of the estimates can be expressed in terms of the input spectrum. This theory can also be used in order to identify the (unknown) excitation, used in a particular experiment, from measured strain data. Two scalar criteria connected to A-and D-optimal experiment design, are considered. The results indicate that the accuracy of the estimates can be greatly improved by applying an optimal input signal. Issues concerning the implementation of the achieved optimal input spectrum in live experiments are discussed briefly.

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“…In his paper, such trajectories were found by a nonlinear optimization problem in which the smallest singular value of a matrix related to the observation matrix was used as a criterion for designing the robot motion. Since then, several papers have focused on the design of exciting trajectories [13][14][15][16][17][18][19][20][21]. The most extended method considers trajectory points guided by a certain analytical function.…”

Section: Introductionmentioning

confidence: 99%

On the Experiment Design for Direct Dynamic Parameter Identification of Parallel Robots

et al. 2009

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Accurate robot dynamic models require the estimation and validation of the dynamic parameters through experiments. To this end, when performing the experiments, the system has to be properly excited so that the unknown parameters can be accurately estimated. The experiment design basically consists of optimizing the trajectory executed by the robot during the experiment. Due to the restricted workspace with parallel robots this task is more challenging than for serial robots; thus, this paper is focused on the experiment design aimed at dynamic parameter identification of parallel robots. Moreover, a multicriteria algorithm is proposed in order to reduce the deficiencies derived from the single-criterion optimization. The results of the identification using trajectories based on a single criterion and the multicriteria approaches are compared, showing that the proposed optimization can be considered as a suitable procedure for designing exciting trajectories for parameter identification.

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Designing G-optimal experiments for robot dynamics identification

Cited by 10 publications

References 6 publications

Adaptive Experimental Design for Path-following Performance Assessment of Unmanned Vehicles

Adaptive Experimental Design for Path-following Performance Assessment of Unmanned Vehicles

Optimal Excitation for Nonparametric Identification of Viscoelastic Materials

On the Experiment Design for Direct Dynamic Parameter Identification of Parallel Robots

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