Reconfigurable Manipulator Simulation for Robotics and Multimodal Machine Learning Application: Aaria

TL;DR

This paper introduces Aaria, a versatile simulated serial manipulator model in Simulink, generating multimodal sensory data for robotics and machine learning applications, supporting various configurations and sensor integrations.

Contribution

The paper presents a systematic, configurable simulation model for serial manipulators that produces multimodal sensory data for machine learning and control research.

Findings

Generated datasets enable advanced robot modeling and control

Supports diverse manipulator configurations with up to 6 DOF

Provides multimodal sensor outputs for deep learning applications

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.