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A Learning-Based Approach for Contact Detection, Localization, and Force Estimation of Continuum Manipulators With Integrated OFDR Optical Fiber

Mobina Tavangarifard, Jonathan S. Kacines, Qiyu Li, Farshid Alambeigi

Abstract

Continuum manipulators (CMs) are widely used in minimally invasive procedures due to their compliant structure and ability to navigate deep and confined anatomical environments. However, their distributed deformation makes force sensing, contact detection, localization, and force estimation challenging, particularly when interactions occur at unknown arc-length locations along the robot. To address this problem, we propose a cascade learning-based framework (CLF) for CMs instrumented with a single distributed Optical Frequency Domain Reflectometry (OFDR) fiber embedded along one side of the robot. The OFDR sensor provides dense strain measurements along the manipulator backbone, capturing strain perturbations caused by external interactions. The proposed CLF first detects contact using a Gradient Boosting classifier and then estimates contact location and interaction force magnitude using a CNN--FiLM model that predicts a spatial force distribution along the manipulator. Experimental validation on a sensorized tendon-driven CM in an obstructed environment demonstrates that a single distributed OFDR fiber provides sufficient information to jointly infer contact occurrence, location, and force in continuum manipulators.

A Learning-Based Approach for Contact Detection, Localization, and Force Estimation of Continuum Manipulators With Integrated OFDR Optical Fiber

Abstract

Continuum manipulators (CMs) are widely used in minimally invasive procedures due to their compliant structure and ability to navigate deep and confined anatomical environments. However, their distributed deformation makes force sensing, contact detection, localization, and force estimation challenging, particularly when interactions occur at unknown arc-length locations along the robot. To address this problem, we propose a cascade learning-based framework (CLF) for CMs instrumented with a single distributed Optical Frequency Domain Reflectometry (OFDR) fiber embedded along one side of the robot. The OFDR sensor provides dense strain measurements along the manipulator backbone, capturing strain perturbations caused by external interactions. The proposed CLF first detects contact using a Gradient Boosting classifier and then estimates contact location and interaction force magnitude using a CNN--FiLM model that predicts a spatial force distribution along the manipulator. Experimental validation on a sensorized tendon-driven CM in an obstructed environment demonstrates that a single distributed OFDR fiber provides sufficient information to jointly infer contact occurrence, location, and force in continuum manipulators.
Paper Structure (18 sections, 11 equations, 6 figures, 4 tables)

This paper contains 18 sections, 11 equations, 6 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Problem Definition and System Overview
  3. Problem Definition
  4. Continuum Manipulator Design and Fabrication
  5. Distributed OFDR Shape-Sensing Assembly
  6. Controlled Contact Generation Setup
  7. Data Acquisition Protocol
  8. Cascade Learning Framework (CLF)
  9. Contact Detection Learning Model
  10. Training Data and Contact Detection Formulation
  11. Contact Classification Model
  12. Contact Localization and Force Estimation Model
  13. Training Data and Model Formulation
  14. Force Localization and Estimation Model
  15. Evaluation Metrics
  16. ...and 3 more sections

Figures (6)

  • Figure 1: A) Conceptual illustration of a continuum manipulator interacting with an obstacle at an unknown location along its geometry. The interaction force and contact location are measured using a force gauge and a motion capture system. An embedded OFDR optical fiber measures the distributed strain along the manipulator. B) Physical experimental setup used to evaluate the proposed CLF. The figure shows the shape-sensing assembly and its integration inside the continuum manipulator (CM).
  • Figure 2: The proposed CLF architecture for contact detection, localization, and magnitude of force estimation.
  • Figure 3: Gradient Boosting Classifier for Contact Detection. Figure illustrates the cascade process using weighted strain data for contact classification.
  • Figure 4: Illustration of the CNN architecture with FiLM generator, processing strain signals and motor position for force localization.
  • Figure 5: Bar plots showing per-test performance for contact classification, including ROC-AUC, Precision, and Recall scores across different test IDs.
  • ...and 1 more figures