一个数据驱动的加速级方案,用于基于图像的视觉伺服操作器的操作器,结构不明
Liuyi Wen1,2, Zhengtai Xie1,3
1The State Key Laboratory of Tibetan Intelligent Information Processing and Application, Qinghai Normal University, Xining, China.
Frontiers in neurorobotics
|April 4, 2024
概括
这项研究引入了对操纵器的数据驱动视觉伺服方案,增强了对结构未知的系统的控制. 拟议的方法在模拟和实验中展示了卓越的学习和控制能力.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 计算机视觉 计算机视觉
- 控制系统 控制系统
背景情况:
- 视觉伺服 (VS) 对于操纵器控制至关重要,但由于加速水平的研究不足而受到限制.
- 现有的VS方法与未知的系统结构作斗争,阻碍了更广泛的应用.
研究的目的:
- 提出一个基于图像的视觉伺服 (AIVS) 方案,考虑联合约束.
- 开发一种数据驱动的方法来估计VS系统中未知的结构信息.
- 整合学习和控制,以建立一个强大的数据驱动的加速级图像基础视觉伺服 (DAIVS) 方案.
主要方法:
- 一个基于二次编程的AIVS方案,包含联合约束.
- 一个数据驱动的学习算法,用于估计未知的结构参数.
- 一个循环神经网络 (RNN) 来实现DAIVS方案,并进行理论稳定性分析.
主要成果:
- 在Franka Emika Panda操纵器上的模拟和实验验证实了拟议的DAIVS方案.
- 与现有方法相比,RNN表现出优越的学习和控制性能.
- 这种方法对于具有未知结构的操纵器来说是可行的和实用的.
结论:
- 拟议的DAIVS方案有效地解决了加速级视觉伺服器的局限性.
- 数据驱动的RNN控制器为具有未知结构信息的操纵器提供了增强的性能.
- 这项研究扩大了视觉伺服在复杂机器人系统中的应用性.
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