基于深度CNN的软机器人的静态建模,使用绝对节点坐标配方.
Haitham El-Hussieny1, Ibrahim A Hameed2, Ayman A Nada1
1Department of Mechatronics and Robotics Engineering, Egypt-Japan University of Science and Technology (E-JUST), Alexandria 21934, Egypt.
Biomimetics (Basel, Switzerland)
|December 22, 2023
概括
这项研究将深度卷积神经网络 (CNN) 与绝对节点坐标配方 (ANCF) 集成,用于软连续机器人的高级建模. 这种方法提高了机器人实时控制和仿真的精度和计算效率.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 计算力学 计算力学 计算力学
- 人工智能的人工智能
背景情况:
- 软连续机器人模仿自然生物以实现适应性运动.
- 精确的建模和控制对于它们的应用至关重要.
- 现有的方法面临着复杂的非线性动态的挑战.
研究的目的:
- 为软连续机器人开发一种高效的反向准静态建模方法.
- 将绝对节点坐标公式 (ANCF) 与深度卷积神经网络 (CNN) 结合起来.
- 为了实现实时模拟和控制静态建模.
主要方法:
- 使用绝对节点坐标公式 (ANCF) 来表示机器人机械.
- 开发和优化深度卷积神经网络 (CNN) 用于反向静态模型.
- 进行了广泛的数值实验和CNN架构的交叉验证.
主要成果:
- 证明了CNN-ANCF方法的计算效率和精度.
- 验证了模型适用于实时模拟和控制的适用性.
- 通过交叉验证确定了最佳的CNN架构.
结论:
- 深度CNN与ANCF的整合为软连续机器人建模提供了显著的优势.
- 这种方法有效地解决了复杂的逆静态学问题.
- 为软机器人中先进的静态建模和控制铺平了道路.
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