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Operation of the Collaborative Composite Manufacturing CCM System
Published on: October 1, 2019
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一个精确预定义的时间收屏障RNN,用于在未知边界噪声下协同控制双臂机器人的位置和方向
IEEE transactions on cybernetics
|November 7, 2025
概括
这项研究引入了双臂机器人的新控制方案,允许精确控制终端效应器位置. 一个新的神经网络确保了即使在噪声下也能快速,准确地融合,验证了其在现实应用中的有效性.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 控制系统 控制系统
- 人工智能的人工智能
背景情况:
- 双臂机器人需要精确的控制来完成诸如对象操纵等任务.
- 现实世界中的机器人系统面临着噪音和时间限制的挑战.
- 现有的控制方法可能不符合严格的性能要求.
研究的目的:
- 为双臂机器人提出一种新的协作位置和方向控制方案 (CPOCS).
- 制定控制策略,确保高精度的位置控制,同时保持方向.
- 解决实时控制挑战,包括未知边界噪声和严格的时间响应.
主要方法:
- 引入一个精确的预定义时间的融合屏障循环神经网络 (PCB-RNN).
- 为PCB-RNN.N.开发一种新的断片式屏障进化公式.
- 理论分析以证明在各种条件下趋同能力.
- 在双臂机器人平台上进行模拟和物理实验.
主要成果:
- 拟议的CPOCS实现了高精度的终端效应器定位.
- 在未知边界噪声下,PCB-RNN精确地证明了预定义时间收 (PPTC).
- 通过广泛的模拟和实验验证控制方案的有效性.
- 在融合速度和准确性方面,PCB-RNN的性能优于现有的循环神经网络.
结论:
- 新的CPOCS是有效的双臂机器人控制.
- 该PCB-RNN提供先进的PPTC功能,对噪声强大.
- 提出的方法为实际的机器人控制应用提供了可靠的解决方案.
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