A Bimodal Detection-Based Tremor Suppression System for Vascular Interventional Surgery Robots

Abstract

Vascular interventional surgery (VIS) robots generally rely on faster and more accurate signal interaction system between the master side and slave side. During actual operations, unconscious and nonlinear tremors limit the accuracy and safety for VIS robot’s operation. To solve above issues, this article proposes a novel bimodal detection-based tremor suppression system to improve the accuracy of data interaction. More precisely, a bimodal tremor detection combined self-designed force sensitive resistor (FSR) and a linear encoder sensor is employed for radial and axial tremor detection, respectively. Tremor suppression method based on active restraint and passive modification is presented in this study, which can not only reduce the regular tremor but also effectively suppress the suddenly burst tremor. Experiments and performance analysis of the proposed method are evaluated from simulations and actual experimental results. Based on these evaluations, the tremor suppression method has obvious advantages for suppressing non-linear tremors and enhancing the timeliness of signal interaction, which the average computing time for tremor is 140 ms and average maximum amplitude moves to 2.47 mm. Moreover, we also discuss the issues for time delay and the transmission loss existed in actual experiments. Besides, this article has a potential impact on eliminating non-linear errors, improving the safety performance of operations for master–slave robotic system, and solving the crossing problem for time–frequency domain.