Enhanced Position Estimation in Tactile Internet-Enabled Remote Robotic Surgery Using MOESP-Based Kalman Filter

2025-01-27

AI Summary

To address inaccurate pose estimation of patient-side manipulators in telesurgery under the Tactile Internet (TI), where network latency, jitter, and packet loss severely degrade estimation performance, this paper proposes a data-driven robust state estimation algorithm. Methodologically, we introduce the MOESP subspace identification technique—previously unexplored in telesurgery—to construct a dynamics-free state-space model of the da Vinci PSM from MTM master input and JIGSAW surgical data, and design a TI-adapted modified Kalman filter. Our key contribution lies in eliminating reliance on precise analytical dynamics models, enabling real-time, high-accuracy pose estimation under concurrent latency, jitter, and packet loss. Experimental evaluation under realistic TI disturbances demonstrates >95% pose estimation accuracy, significantly enhancing the filter’s adaptability to time-varying uncertainties and its real-time capability.

Technology Category

• Intelligent Robots: State Estimation
• Computer Vision: Motion & Tracking
• Planning, Routing, and Scheduling: Activity and Plan Recognition

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Accurately estimating the position of a patient's side robotic arm in real time during remote surgery is a significant challenge, especially within Tactile Internet (TI) environments. This paper presents a new and efficient method for position estimation using a Kalman Filter (KF) combined with the Multivariable Output-Error State Space (MOESP) method for system identification. Unlike traditional approaches that require prior knowledge of the system's dynamics, this study uses the JIGSAW dataset, a comprehensive collection of robotic surgical data, along with input from the Master Tool Manipulator (MTM) to derive the state-space model directly. The MOESP method allows accurate modeling of the Patient Side Manipulator (PSM) dynamics without prior system models, improving the KF's performance under simulated network conditions, including delays, jitter, and packet loss. These conditions mimic real-world challenges in Tactile Internet applications. The findings demonstrate the KF's improved resilience and accuracy in state estimation, achieving over 95 percent accuracy despite network-induced uncertainties.

Problem

• Tele-surgery
• Haptic Internet
• Positioning Accuracy

Innovation

• MOESP method
• Kalman Filter
• Tactile Internet