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

Muhammad Hanif Lashari, Wafa Batayneh, Ashfaq Khokhar, Shakil Ahmed

TL;DR

This work tackles real-time, high-precision position estimation for the patient-side manipulator in TI-enabled remote robotic surgery. It introduces a KF augmented by MOESP-based system identification to derive a data-driven state-space model from the JIGSAWS dataset, removing the need for prior dynamics. Empirical estimation of process and measurement noise preserves filter robustness under network delays, jitter, and packet loss, with reported position accuracies predominantly above 95% across X, Y, and Z axes. The approach offers a computationally efficient alternative to deep learning or Gaussian-process methods, enabling reliable remote surgery performance in challenging TI networks and suggesting avenues for adaptive filtering and lightweight ML in future TI workflows.

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.

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

TL;DR

This work tackles real-time, high-precision position estimation for the patient-side manipulator in TI-enabled remote robotic surgery. It introduces a KF augmented by MOESP-based system identification to derive a data-driven state-space model from the JIGSAWS dataset, removing the need for prior dynamics. Empirical estimation of process and measurement noise preserves filter robustness under network delays, jitter, and packet loss, with reported position accuracies predominantly above 95% across X, Y, and Z axes. The approach offers a computationally efficient alternative to deep learning or Gaussian-process methods, enabling reliable remote surgery performance in challenging TI networks and suggesting avenues for adaptive filtering and lightweight ML in future TI workflows.

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.

Paper Structure

This paper contains 24 sections, 6 equations, 7 figures, 3 tables, 2 algorithms.

Table of Contents

  1. INTRODUCTION
  2. RELATED WORK
  3. METHODOLOGY
  4. System Model
  5. KF Implementation for State Estimation with TI Network Effects
  6. Empirical Estimation of Q and R
  7. SOURCE AND COMPARISON OF THE JIGSAW DATASET
  8. SUBSPACE IDENTIFICATION USING MOESP
  9. Introduction to Subspace Identification
  10. DATA ORGANIZATION AND MATRIX CONSTRUCTION
  11. Importance of Hankel matrices
  12. Identification Method for State-Space Model Estimation
  13. Validation of Model Predictions
  14. Data Preprocessing
  15. RESULTS
  16. ...and 9 more sections

Figures (7)

  • Figure 1: Remote Robotic Surgery Framework Utilizing TI and KF for Enhanced PSM Precision
  • Figure 2: Operational Sequence of Remote Surgery Using TI and KF Algorithm
  • Figure 3: Three surgical tasks: (a) Suturing, (b) Knot-tying and (c) Needle-passing 21.
  • Figure 4: Singular Values from MOESP Algorithm
  • Figure 5: Comparison of PSM data with the KF estimated output for Position x
  • ...and 2 more figures