一项关于连续机器人设计,执行,建模和控制的调查
Jingyu Zhang1,2, Qin Fang1,2, Pingyu Xiang1,2
1State Key Laboratory of Industrial Control and Technology, Zhejiang University, Hangzhou 310027, China.
Cyborg and bionic systems (Washington, D.C.)
|April 15, 2024
概括
连续机器人正在推进新的设计,执行和控制方法. 机器学习正在简化它们的建模和控制,增强未来应用程序的功能.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 机械工程 机械工程
- 控制系统 控制系统
背景情况:
- 连续机器人经历了数十年的研究,导致了创新的设计和执行方法.
- 无绳磁力机器人和自感连续机器人由于其独特的特性而引起了学术兴趣.
- 在这些机器人的建模和控制技术方面取得了重大进展.
研究的目的:
- 审查连续机器人设计,执行,建模和控制方面的最新进展.
- 突出机器学习等新技术对连续机器人开发的影响.
- 讨论当前的挑战和该领域的未来前景.
主要方法:
- 审查创新的结构设计和执行方法,包括无绳磁力机器人.
- 基于连续力学和几何形状假设的建模方法的讨论.
- 控制策略的分析,包括机器学习实现的闭环,混合和数据驱动的方法.
主要成果:
- 使用连续力学和几何假设进行建模的进展,使基于模型的有效控制成为可能.
- 控制方法的进步 (闭环,混合) 通过传感器反提供了更好的准确性和弹性.
- 机器学习促进了数据驱动的建模,简化了流程,并增强了概括和反干扰能力.
结论:
- 连续机器人技术在设计,执行,建模和控制方面取得了实质性的发展.
- 机器学习为简化和更强大的连续机器人系统提供了有希望的途径.
- 该领域面临着持续的挑战,但对未来的创新有很大的潜力.
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