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

Research output: Contribution to journalArticleScientific

Details

Original languageEnglish
Article number1803.00532
JournalarXiv.org
Publication statusE-pub ahead of print - 1 Mar 2018
Publication typeB1 Article in a scientific magazine

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