On the effect of vibration on shape sensing of continuum manipulators using fiber Bragg gratings

Sefati, Shahriar; Alambeigi, Farshid; Iordachita, Iulian; Taylor, Russell H.; Armand, Mehran

doi:10.1109/ismr.2018.8333303

Cited by 14 publications

(13 citation statements)

References 23 publications

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“…Figure 3 shows the principle of the wavelength detection of FBG sensor. According to the previous literatures (Amanzadeh et al, 2018;Sefati et al, 2018), FBG sensor can reflect of the wavelength shift of the reflected light when the ambient temperature and structural strain change, which is illustrated in equation ( 5).…”

Section: Shape Reconstruction Based On Fiber Bragg Grating Sensorsmentioning

confidence: 99%

“…The strain detection range is expanded by changing the wavelength range of FBG sensor. At present, although the FBG sensor (Amanzadeh et al, 2018;Sefati et al, 2018) has been used for the shape sensing of the continuum robot, the current literatures only analyzed the shape of the robot when it deforms in free space with ignoring the influence of the external load on the shape deformation. Moreover, the shape reconstruction model of the robot is relatively complex and its solving process is tedious.…”

Section: Introductionmentioning

confidence: 99%

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Shape sensing and feedback control of the catheter robot for interventional surgery

Bai

She

et al. 2020

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Purpose This paper aims to present a shape sensing method and feedback control strategy based on fiber Bragg grating (FBG) sensor to improve the control accuracy of the robot and ensure the safety of the cardiac interventional surgery. Design/methodology/approach To theoretically describe the shape of the catheter robot, the kinematic model is established by the geometric analysis method. And to obtain the actual shape, a large curvature assemble sensor based on FBG is adopted and a novel simple shape reconstruction model is proposed, which can provide the shape curve and distal position. In addition, the influence of external load on the bending deformation is investigated by experiments. To improve the shape accuracy of the robot, a shape feedback control method is presented to control the catheter robot, which can control the robot to bend into the pre-given desired shape. Findings Experiment results verify the effectiveness of the shape sensing method and the reconstruction model, and the correlation coefficients of three sets of curve in different coordinate directions are 0.9986, 0.9992 and 0.9999. Results of the shape feedback experiment show that the curvature error and direction angle error are 1.42% and 10.3%, respectively. The continuum catheter robot can be controlled to achieve the desired bending shape. Originality/value The shape reconstruction method and feedback control strategy proposed in this paper can improve the control accuracy of the robot to avoid the risk of the collision with the surrounding blood vessels, the tissues and organs.

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Section: Shape Reconstruction Based On Fiber Bragg Grating Sensorsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Shape sensing and feedback control of the catheter robot for interventional surgery

Bai

She

et al. 2020

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“…A variety of FBG sensor manufacturing techniques associated with the choice of substrate material, geometry, number of fibers and their attachments have been explored in the literature [4]- [12]. Liu et al [4] used a triangular configuration ( Fig.…”

Section: Introductionmentioning

confidence: 99%

FBG-Based Position Estimation of Highly Deformable Continuum Manipulators: Model-Dependent vs. Data-Driven Approaches

Sefati

Hegeman

Alambeigi

et al. 2019

2019 International Symposium on Medical Robotics (ISMR)

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Conventional shape sensing techniques using Fiber Bragg Grating (FBG) involve finding the curvature at discrete FBG active areas and integrating curvature over the length of the continuum dexterous manipulator (CDM) for tip position estimation (TPE). However, due to limited number of sensing locations and many geometrical assumptions, these methods are prone to large error propagation especially when the CDM undergoes large deflections. In this paper, we study the complications of using the conventional TPE methods that are dependent on sensor model and propose a new data-driven method that overcomes these challenges. The proposed method consists of a regression model that takes FBG wavelength raw data as input and directly estimates the CDM's tip position. This model is pre-operatively (off-line) trained on position information from optical trackers/cameras (as the ground truth) and it intraoperatively (on-line) estimates CDM tip position using only the FBG wavelength data. The method's performance is evaluated on a CDM developed for orthopedic applications, and the results are compared to conventional model-dependent methods during large deflection bendings. Mean absolute TPE error (and standard deviation) of 1.52 (0.67) mm and 0.11 (0.1) mm with maximum absolute errors of 3.63 mm and 0.62 mm for the conventional and the proposed data-driven techniques were obtained, respectively. These results demonstrate a significant out-performance of the proposed data-driven approach versus the conventional estimation technique.

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“…In recent years, FBG optical sensors have offered advantages over other sensing methods such as not requiring a direct line of sight, high streaming rate (up to 1 KHz), and minimal effects on compliance and compactness. FBG sensors have been used for shape, force, and torsion sensing in applications with CMs, biopsy needles, catheters, and other flexible medical instruments [7,8,9,12,13,14,19]. To this end, we implement our proposed method on a CM previously developed for MIS applications, equipped with FBG sensors for shape estimation [15,16,17].…”

Section: Introductionmentioning

confidence: 99%

Learning to Detect Collisions for Continuum Manipulators Without a Prior Model

Sefati

Iordachita

et al. 2019

Lecture Notes in Computer Science

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Due to their flexibility, dexterity, and compact size, Continuum Manipulators (CMs) can enhance minimally invasive interventions. In these procedures, the CM may be operated in proximity of sensitive organs; therefore, requiring accurate and appropriate feedback when colliding with their surroundings. Conventional CM collision detection algorithms rely on a combination of exact CM constrained kinematics model, geometrical assumptions such as constant curvature behavior, a priori knowledge of the environmental constraint geometry, and/or additional sensors to scan the environment or sense contacts. In this paper, we propose a data-driven machine learning approach using only the available sensory information, without requiring any prior geometrical assumptions, model of the CM or the surrounding environment. The proposed algorithm is implemented and evaluated on a non-constant curvature CM, equipped with Fiber Bragg Grating (FBG) optical sensors for shape sensing purposes. Results demonstrate successful detection of collisions in constrained environments with soft and hard obstacles with unknown stiffness and location.

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On the effect of vibration on shape sensing of continuum manipulators using fiber Bragg gratings

Cited by 14 publications

References 23 publications

Shape sensing and feedback control of the catheter robot for interventional surgery

Shape sensing and feedback control of the catheter robot for interventional surgery

FBG-Based Position Estimation of Highly Deformable Continuum Manipulators: Model-Dependent vs. Data-Driven Approaches

Learning to Detect Collisions for Continuum Manipulators Without a Prior Model

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