Enhancing Precision in Tactile Internet-Enabled Remote Robotic Surgery: Kalman Filter Approach

TL;DR

This paper introduces a Kalman Filter-based method for real-time, accurate position estimation of robotic arms in remote surgery over Tactile Internet, demonstrating robustness under network challenges with over 90% accuracy.

Contribution

It proposes a novel, computationally efficient Kalman Filter approach that does not require prior system knowledge, utilizing system identification for improved remote surgical robot positioning.

Findings

Achieves over 90% estimation accuracy under network delays and packet loss.

Demonstrates robustness of Kalman Filter in realistic Tactile Internet conditions.

Utilizes system identification to learn system models without prior knowledge.

Abstract

Accurately estimating the position of a patient's side robotic arm in real time in a remote surgery task is a significant challenge, particularly in Tactile Internet (TI) environments. This paper presents a Kalman Filter (KF) based computationally efficient position estimation method. The study also assume no prior knowledge of the dynamic system model of the robotic arm system. Instead, The JIGSAW dataset, which is a comprehensive collection of robotic surgical data, and the Master Tool Manipulator's (MTM) input are utilized to learn the system model using System Identification (SI) toolkit available in Matlab. We further investigate the effectiveness of KF to determine the position of the Patient Side Manipulator (PSM) under simulated network conditions that include delays, jitter, and packet loss. These conditions reflect the typical challenges encountered in real-world Tactile Internet applications. The results of the study highlight KF's resilience and effectiveness in achieving accurate state estimation despite network-induced uncertainties with over 90\% estimation accuracy.