面向机器人和多模态机器学习应用的可重构机械手仿真：Aaria
Reconfigurable Manipulator Simulation for Robotics and Multimodal Machine Learning Application: Aaria
摘要：
本文提出了一种系统的方法来产生的Aaria，一个仿真模型的串联机械手的运动学或动态分析的目的与各种各样的结构的基础上Simulink SimMechanics。所提出的模型可以接收配置参数，例如根据修改后的Denavit-Hartenberg公约，或其基础或关节的轨迹与1至6自由度（DOF）的结构。机械手配备了人工关节传感器以及模拟惯性测量单元（IMU）的每个环节。仿真输出可以是关节空间中的位置、速度、扭矩或IMU输出;角速度、线加速度、相对于惯性系的工具坐标。该仿真模型是用于机器人建模和控制自动化的虚拟多模态传感数据的数据集的来源，该数据集被设计用于基于大数据的机器学习和深度学习方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
