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

TL;DR

This paper introduces a learning-based framework using optical fiber sensors to detect, localize, and estimate forces in continuum manipulators, enhancing minimally invasive surgical tools' interaction capabilities.

Contribution

It presents a novel cascade learning framework that leverages dense strain data from a single optical fiber to jointly detect contact, localize it, and estimate force in continuum robots.

Findings

High accuracy in contact detection and localization.

Effective force estimation along the manipulator.

Single fiber sensor suffices for multiple sensing tasks.

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.