Reconfigurable Manipulator Simulation for Robotics and Multimodal Machine Learning Application: 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
Cite
@article{arxiv.1803.00532,
title = {Reconfigurable Manipulator Simulation for Robotics and Multimodal Machine Learning Application: Aaria},
author = {Arttu Hautakoski and Mohammad M. Aref and Jouni Mattila},
journal= {arXiv preprint arXiv:1803.00532},
year = {2018}
}
Comments
preprint before submission to conference: 2018 IEEE International Conference on Automation Science and Engineering , 7 pages