ARKOMA数据集:一个开源数据集,用于开发基于神经网络的逆动力学模型,用于NAO机器人手臂
Arif Nugroho1, Eko Mulyanto Yuniarno1,2, Mauridhi Hery Purnomo1,2,3
1Department of Electrical Engineering, Institut Teknologi Sepuluh Nopember (ITS), Surabaya, Indonesia.
Data in brief
|November 29, 2023
概括
这项研究介绍了ARKOMA,这是一个用于训练神经网络的新数据集,用于模拟NAO机器人手臂的逆动力学. 这种资源使得开发精确的机器人运动控制系统成为可能.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 机器学习 机器学习
- 控制系统 控制系统
背景情况:
- 反向动力学对于机器人运动的规划和控制至关重要.
- 神经网络提供了一个灵活的,数据驱动的方法来建模复杂的动力学关系.
- 对于NAO机器人臂的现有方法需要强大的数据集来进行有效的反向动力学建模.
研究的目的:
- 介绍ARKOMA数据集,专门设计用于开发基于神经网络的NAO机器人手臂的逆动力学模型.
- 提供一个全面的数据集,使研究人员能够训练和验证反动力学解决方案.
- 为了促进NAO平台的机器人运动控制方面的进步.
主要方法:
- 创建ARKOMA数据集,包括10,000个输入输出数据对.
- 输入数据:终端效应器的位置和方向.
- 输出数据:相应的联合角度位置.
- 数据集分区:60%的培训,20%的验证,20%的测试.
主要成果:
- ARKOMA数据集包含1万个适合训练神经网络的数据点.
- 数据集的结构是为了训练,验证和测试反向动力学模型.
- 该数据集适用于NAO H25 v3.3和更新的机器人手臂版本.
结论:
- ARKOMA数据集是推进NAO机器人手臂基于神经网络的逆动力学的宝贵资源.
- 这一数据集的可用性将加速机器人运动控制领域的研究和开发.
- 这笔捐款支持创建更复杂,更准确的机器人控制系统.
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