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

2026-03-12

AI Summary

Continuum manipulators pose significant challenges in accurately detecting contact, localizing interaction points, and estimating applied forces due to their distributed deformation, particularly when interactions occur at unknown arc-length positions. This work proposes a cascaded learning framework that, for the first time, integrates a single embedded OFDR (optical frequency-domain reflectometry) fiber with a gradient-boosting classifier and a CNN-FiLM network. The approach first employs the classifier to detect contact events and then leverages the CNN-FiLM architecture to simultaneously estimate both contact location and force magnitude. Experimental results demonstrate that this method enables high-precision joint inference of contact states using only a single OFDR fiber, offering notable advantages in spatial force distribution perception.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Humans and AI: Interaction Techniques and Devices
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Machine-in-the-loop, human agency and autonomy

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.

Problem

• continuum manipulators
• contact detection
• force estimation
• distributed sensing
• OFDR

Innovation

• continuum manipulator
• OFDR fiber
• contact detection
• force estimation
• cascade learning framework