Výsledky vyhledávání - "Brüls, Olivier"

Dissertation/ Thesis

Integrated Simulation and Reduced-Order Modeling of Controlled Flexible Multibody Systems

Autor: Bruls, Olivier

A mechatronic system is an assembly of technological components, such as a mechanism, sensors, actuators, and a control unit. Recently, a number of researchers and industrial manufacturers have highlighted the potential advantages of lightweight para

Externí odkaz: http://bictel.ulg.ac.be/ETD-db/collection/available/ULgetd-07092007-155827/

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Invariant Kalman Filtering with Noise-Free Pseudo-Measurements

Autor: Goffin, Sven, Bonnabel, Silvère, Brüls, Olivier, Sacré, Pierre

Publikováno v: 62nd IEEE Conference on Decision and Control (CDC), Singapore, Singapore, 2023, pp. 8665-8671

In this paper, we focus on developing an Invariant Extended Kalman Filter (IEKF) for extended pose estimation for a noisy system with state equality constraints. We treat those constraints as noise-free pseudo-measurements. To this aim, we provide a

Externí odkaz: http://arxiv.org/abs/2404.10687

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Iterated Invariant Extended Kalman Filter (IIEKF)

Autor: Goffin, Sven, Barrau, Axel, Bonnabel, Silvère, Brüls, Olivier, Sacré, Pierre

In this paper, we introduce the Iterated Invariant Extended Kalman Filter (IIEKF), which is an invariant extended Kalman filter (IEKF) where the updated state in the light of the latest measurement is defined as a maximum a posteriori (MAP) estimate.

Externí odkaz: http://arxiv.org/abs/2404.10665

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Implicit representation priors meet Riemannian geometry for Bayesian robotic grasping

Autor: Marlier, Norman, Gustin, Julien, Brüls, Olivier, Louppe, Gilles

Robotic grasping in highly noisy environments presents complex challenges, especially with limited prior knowledge about the scene. In particular, identifying good grasping poses with Bayesian inference becomes difficult due to two reasons: i) genera

Externí odkaz: http://arxiv.org/abs/2304.08805

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Simulation-based Bayesian inference for robotic grasping

Autor: Marlier, Norman, Brüls, Olivier, Louppe, Gilles

General robotic grippers are challenging to control because of their rich nonsmooth contact dynamics and the many sources of uncertainties due to the environment or sensor noise. In this work, we demonstrate how to compute 6-DoF grasp poses using sim

Externí odkaz: http://arxiv.org/abs/2303.05873

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Simulation-based Bayesian inference for multi-fingered robotic grasping

Autor: Marlier, Norman, Brüls, Olivier, Louppe, Gilles

Multi-fingered robotic grasping is an undeniable stepping stone to universal picking and dexterous manipulation. Yet, multi-fingered grippers remain challenging to control because of their rich nonsmooth contact dynamics or because of sensor noise. I

Externí odkaz: http://arxiv.org/abs/2109.14275

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