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3 results on '"Kevin Subrin"'

1. On the Improvement of ROS-Based Control for Teleoperated Yaskawa Robots

Author

Sana Baklouti, Guillaume Gallot, Julien Viaud, and Kevin Subrin

Subjects
teleoperation, latency analysis, Robot Operating System, ROS-based control, Technology, Engineering (General). Civil engineering (General), TA1-2040, Biology (General), QH301-705.5, Physics, QC1-999, Chemistry, QD1-999
Abstract

This paper deals with Yaskawa robots controlling the Robot Operating System (ROS) for teleoperation tasks. The integration of an open-source ROS interface based on standard Motoman packages into control loop leads to large trajectory tracking errors and latency, which are unsuitable for robotic teleoperation. An improved version of the standard ROS-based control is proposed by adding a new velocity control mode into the standard Motoman ROS driver. These two approaches are compared in terms of response time and tracking delay. Investigations applied on the Yaskawa GP8 robot while using the proposed improved ROS-based control confirmed trajectory tracking and latency improvements, which can achieve 43% with respect to standard control.

Published
2021
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2. Transparency Analysis of a Passive Heavy Load Comanipulation Arm

Author

Thomas Muller, Kevin Subrin, Denis Joncheray, Alain Billon, Sebastien Garnier, NEODITECH, Laboratoire des Sciences du Numérique de Nantes (LS2N), Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-École Centrale de Nantes (Nantes Univ - ECN), Nantes Université (Nantes Univ)-Nantes Université (Nantes Univ)-Nantes université - UFR des Sciences et des Techniques (Nantes univ - UFR ST), Nantes Université - pôle Sciences et technologie, Nantes Université (Nantes Univ)-Nantes Université (Nantes Univ)-Nantes Université - pôle Sciences et technologie, Nantes Université (Nantes Univ), Robots and Machines for Manufacturing, Society and Services (LS2N - équipe RoMas), Nantes Université (Nantes Univ)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), and École Centrale de Nantes (Nantes Univ - ECN)

Subjects
Human-Computer Interaction, [SPI]Engineering Sciences [physics], Artificial Intelligence, Computer Networks and Communications, Control and Systems Engineering, Signal Processing, Human Factors and Ergonomics, Computer Science Applications
Abstract

International audience

Published
2022
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3. REINFORCEMENT LEARNING BASED POINT-CLOUD ACQUISITION AND RECOGNITION USING EXPLORATION-CLASSIFICATION REWARD COMBINATION

Author

Kevin Riou, Kevin Subrin, Patrick Le Callet, Nantes Université - Ecole Polytechnique de l'Université de Nantes (Nantes Univ - EPUN), Nantes Université - pôle Sciences et technologie, Nantes Université (Nantes Univ)-Nantes Université (Nantes Univ), École Centrale de Nantes (Nantes Univ - ECN), Nantes Université (Nantes Univ), Image Perception Interaction (LS2N - équipe IPI), Laboratoire des Sciences du Numérique de Nantes (LS2N), Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-École Centrale de Nantes (Nantes Univ - ECN), Nantes Université (Nantes Univ)-Nantes Université (Nantes Univ)-Nantes université - UFR des Sciences et des Techniques (Nantes univ - UFR ST), Nantes Université (Nantes Univ)-Nantes Université (Nantes Univ)-Nantes Université - pôle Sciences et technologie, Nantes Université (Nantes Univ)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), and Robots and Machines for Manufacturing, Society and Services (LS2N - équipe RoMas)

Subjects
[INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], Reinforcement Learning, Active point-clouds acquisition, 3D objects recognition
Abstract

International audience; 3D points acquisitions based on robust sensors such as tactile or laser sensors are true alternatives to computer vision for 3D object recognition. In real life scenarios where robots are equipped with such sensors to acquire 3D data, only few points can be iteratively collected in a reasonable amount of time. However, existing Point-cloud classifiers are extremely sensitive to sparse points, missing parts and noise. To compensate for the sparsity of the data, some Reinforcement Learning (RL) based approaches have been proposed to learn a sparse yet efficient exploration of the target object regarding the 3D recognition objective. However, existing RL approaches only focus on classification performances to guide the training of the active acquisition-and-classification frameworks, and thus fail to dissociate poor exploration strategy (missing parts, noisy points) from actual classifier mistakes on proper data. In this study, we proposed a new RL framework that was rewarded regarding both the classification performances and the exploration quality. Our trained framework outperforms existing State-Of-The-Art models on 3D geometric objects classification. We further showed that our trained framework learnt to alternate between (1) a clean and broad exploration strategy, suitable for easily distinguishable categories, and (2) a specific local exploration strategy, facilitating the discrimination of similar categories.

Published
2022

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