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Reconfigurable manipulator simulation for robotics and multimodal machine learning application: Aaria: Aaria

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Reconfigurable manipulator simulation for robotics and multimodal machine learning application: Aaria : Aaria. / Hautakoski, Arttu; Aref, Mohammad M.; Mattila, Jouni.

In: arXiv.org, 01.03.2018.

Research output: Contribution to journalArticleScientific

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@article{7dddcac4e6114d9490965dd69e36081d,
title = "Reconfigurable manipulator simulation for robotics and multimodal machine learning application: Aaria: Aaria",
abstract = "This paper represents a systematic way for generation of Aaria, a simulated model for serial manipulators for the purpose of kinematic or dynamic analysis with a vast variety of structures based on Simulink SimMechanics. The proposed model can receive configuration parameters, for instance in accordance with modified Denavit-Hartenberg convention, or trajectories for its base or joints for structures with 1 to 6 degrees of freedom (DOF). The manipulator is equipped with artificial joint sensors as well as simulated Inertial Measurement Units (IMUs) on each link. The simulation output can be positions, velocities, torques, in the joint space or IMU outputs; angular velocity, linear acceleration, tool coordinates with respect to the inertial frame. This simulation model is a source of a dataset for virtual multimodal sensory data for automation of robot modeling and control designed for machine learning and deep learning approaches based on big data.",
keywords = "Machine learning, Simulation, Robotics, Simulink, Deep learning, Multimodal interaction, Big data, Velocity, Sensor, Denavit–Hartenberg",
author = "Arttu Hautakoski and Aref, {Mohammad M.} and Jouni Mattila",
note = "preprint before submission to conference: 2018 IEEE International Conference on Automation Science and Engineering , 7 pages",
year = "2018",
month = "3",
day = "1",
language = "English",
journal = "arXiv.org",
issn = "2331-8422",

}

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TY - JOUR

T1 - Reconfigurable manipulator simulation for robotics and multimodal machine learning application: Aaria

T2 - Aaria

AU - Hautakoski, Arttu

AU - Aref, Mohammad M.

AU - Mattila, Jouni

N1 - preprint before submission to conference: 2018 IEEE International Conference on Automation Science and Engineering , 7 pages

PY - 2018/3/1

Y1 - 2018/3/1

N2 - This paper represents a systematic way for generation of Aaria, a simulated model for serial manipulators for the purpose of kinematic or dynamic analysis with a vast variety of structures based on Simulink SimMechanics. The proposed model can receive configuration parameters, for instance in accordance with modified Denavit-Hartenberg convention, or trajectories for its base or joints for structures with 1 to 6 degrees of freedom (DOF). The manipulator is equipped with artificial joint sensors as well as simulated Inertial Measurement Units (IMUs) on each link. The simulation output can be positions, velocities, torques, in the joint space or IMU outputs; angular velocity, linear acceleration, tool coordinates with respect to the inertial frame. This simulation model is a source of a dataset for virtual multimodal sensory data for automation of robot modeling and control designed for machine learning and deep learning approaches based on big data.

AB - This paper represents a systematic way for generation of Aaria, a simulated model for serial manipulators for the purpose of kinematic or dynamic analysis with a vast variety of structures based on Simulink SimMechanics. The proposed model can receive configuration parameters, for instance in accordance with modified Denavit-Hartenberg convention, or trajectories for its base or joints for structures with 1 to 6 degrees of freedom (DOF). The manipulator is equipped with artificial joint sensors as well as simulated Inertial Measurement Units (IMUs) on each link. The simulation output can be positions, velocities, torques, in the joint space or IMU outputs; angular velocity, linear acceleration, tool coordinates with respect to the inertial frame. This simulation model is a source of a dataset for virtual multimodal sensory data for automation of robot modeling and control designed for machine learning and deep learning approaches based on big data.

KW - Machine learning

KW - Simulation

KW - Robotics

KW - Simulink

KW - Deep learning

KW - Multimodal interaction

KW - Big data

KW - Velocity

KW - Sensor

KW - Denavit–Hartenberg

M3 - Article

JO - arXiv.org

JF - arXiv.org

SN - 2331-8422

M1 - 1803.00532

ER -