一个数据驱动的分布式循环神经网络,用于多个多余操纵器的协作系统,结构不明.
IEEE transactions on cybernetics
|August 14, 2025
概括
这项研究引入了一种新的神经网络,用于精确的机器人手臂协作运动,即使有未知的参数. 该方法提高了多操纵器系统的准确性和适用性.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 人工智能的人工智能
- 控制系统 控制系统
背景情况:
- 在多操纵器系统 (MMC) 中,精确的协作运动生成是具有挑战性的,尤其是在未知系统参数的情况下.
- 传统方法通常需要准确的模型或依赖于有限的雅可比式估计技术.
- 现有的数据驱动方法与机器人系统的动态性,时间变化的性质作斗争.
研究的目的:
- 提出一种新的数据驱动的分布式循环神经网络 (DDD-RNN),用于在MMC中精确的协作运动生成.
- 解决现有方法的局限性,允许在线估计一级和二级雅可比矩阵.
- 开发一种基于神经动力学的循环神经网络解决方案,能够解释时间变化的操纵器特征.
主要方法:
- 一个新的数据驱动分布式循环神经网络 (DDD-RNN) 的设计,采用神经动力学原理.
- 开发了一种改进的雅可比矩阵估计定律 (IJM),用于同时在线估计一级和二级雅可比矩阵.
- 实现使用神经动力学标准处理时间变化的信息的循环神经网络解决方案.
主要成果:
- 在Ufactory XArm6机器人的模拟和实验中,DDD-RNN方法成功生成了协作运动.
- 这种方法证明了可行性,即使机器人手臂的结构参数是未知的.
- 在线对一级和二级雅可比矩阵的估计有效地捕获了操纵者的时间变化动态.
结论:
- 与传统和现有的数据驱动技术相比,拟议的DDD-RNN方法为MMC中精确的协作运动生成提供了一种优越的方法.
- 该方法提高了终端效应器的准确性和适用性,特别是在动态不明的系统中.
- DDD-RNN为需要精确的多臂协调的现实世界机器人应用提供了强大的解决方案.
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