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

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arxiv 2406.04503 v1 pith:FB26J2XX submitted 2024-06-06 cs.RO cs.ETcs.LGcs.SYeess.SY

Muhammad Hanif Lashari , Wafa Batayneh , Ashfaq Khokhar This is my paper · ORCID
classification cs.ROcs.ETcs.LGcs.SYeess.SY
keywords roboticsystemestimationpositiontactileconditionseffectivenessfilter
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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.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. A Predictive Approach for Enhancing Accuracy in Remote Robotic Surgery Using Informer Model

    cs.RO 2025-01 reject novelty 4.0 of 10

    An Informer transformer predicts surgical tool-tip position under simulated packet loss and is reported to exceed 90% accuracy on JIGSAWS knot-tying trials.

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

    cs.RO 2025-01 reject novelty 3.0 of 10

    A MOESP-identified state-space model plus Kalman filter is applied to the JIGSAWS dataset, but the reported position estimation accuracy falls below 95 percent under several of the paper's own simulated network scenarios.

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