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Modal-Space Filtering by Expectation Value Extraction for High-Scaling Bilateral Control

机译:通过期望值提取进行模态空间滤波以进行大规模双边控制

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This paper proposes a novel filtering method to reduce the stochastic disturbance in modal space. Recently, macro-micro manipulation has been applied in medical fields, especially for cell manipulation. In conventional approaches, a scaling bilateral control with haptic feedback is one of the key technologies for micro manipulation. However, the quality of haptic information deteriorates because of the influence of stochastic disturbance such as quantization noise caused by the interference between the macro-space and micro-space. To solve this problem, a modal filter that compensates for interference terms in each space is proposed. By dealing with the random variable as a mass fluctuation, the interference term of the stochastic disturbance on the master system can be clarified. Using the proposed method, it is possible to reduce the variance of the random variable and extract the expectation value. The validity of the proposed method is confirmed by simulation and experimental results.
机译:本文提出了一种减少模态空间随机干扰的新型滤波方法。近来,宏观微操纵已在医学领域中应用,特别是用于细胞操纵。在传统方法中,具有触觉反馈的缩放双边控制是微操作的关键技术之一。然而,由于诸如由宏空间和微空间之间的干扰引起的量化噪声之类的随机干扰的影响,触觉信息的质量劣化。为了解决该问题,提出了一种补偿每个空间中的干扰项的模态滤波器。通过将随机变量视为质量波动,可以弄清随机扰动对主系统的干扰项。使用所提出的方法,可以减小随机变量的方差并提取期望值。仿真和实验结果验证了该方法的有效性。

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