A Learning-Based Approach for Contact Detection, Localization, and Force Estimation of Continuum Manipulators With Integrated OFDR Optical Fiber
Authors: Mobina Tavangarifard, Jonathan S. Kacines, Qiyu Li, Farshid Alambeigi
Organizations: Walker Department of Mechanical Engineering and Texas Robotics, The University of Texas at Austin, Austin, TX 78712, USA. · Department of Computer Science, The University of Texas at Austin, Austin, TX 78712, USA.
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.
Figures & tables
Fig. 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).
Fig. 2: The proposed CLF architecture for contact detection, localization, and magnitude of force estimation.
Fig. 3: Gradient Boosting Classifier for Contact Detection. Figure illustrates the cascade process using weighted strain data for contact classification.
Fig. 4: Illustration of the CNN architecture with FiLM generator, processing strain signals and motor position for force localization.
Block
In Ch.
Out Ch.
Kernel
Padding
Dilation
Block 1
1
16
7
3
2
Block 2
16
32
7
3
2
Block 3
32
64
3
1
1
TABLE I: Convolutional Block Architecture
Layer
In Features
Out Features
Purpose
Linear 1
Nscalar
128
Feature projection
Linear 2
128
128
Output γ and β
TABLE II: FiLM Generator Architecture
Layer
In Features
Out Features
Activation
Linear 1
DimFiLM
128
ReLU
Linear 2
128
64
ReLU
Linear 3
64
Nout
–
TABLE III: Fully Connected Layer Architecture
Fig. 5: Bar plots showing per-test performance for contact classification, including ROC-AUC, Precision, and Recall scores across different test IDs.
Fig. 6: Force and localization absolute error distributions. Mean absolute error (MAE) indicated by horizontal line.
Label
ROC -AUC
Recall
Precision
Force MAE (N)
Localization MAE (mm)
cv1
0.995
0.943
0.957
0.013
2.936
cv2
0.996
0.989
0.913
0.008
0.494
cv3
0.963
0.692
1.000
0.017
0.646
cv4
0.983
0.824
0.913
0.008
6.477
cv5
0.990
0.745
1.000
0.009
0.497
cv6
0.927
0.570
0.880
0.014
0.728
TABLE IV: Contact Classification Performance Metrics, Force and Localization Errors
Micro-scale continuum robots face significant limitations in achieving three-dimensional contact force perception, primarily due to structural miniaturization, nonlinear mechanical, and sensor integration. To overcome these limitations, this paper introduces a novel proprioception method for cable-driven continuum robots based on proximal-integrated force sensing (i.e., cable tension and six-axis force/torque (F/T) sensor), inspired by the tendon-joint collaborative sensing mechanism of the finger. By integrating biomechanically inspired design principles with nonlinear modeling, the proposed method addresses the challenge of force perception (including the three-dimensional contact force and the location of the contact point) and shape estimation in micro-scale continuum robots. First, a quasi-bionic mapping between human tissues/organs and robot components is established, enabling the transfer of the integrated sensing strategy of tendons, joints, and neural feedback to the robotic system. Second, a multimodal perception strategy is developed based on the structural constraints inherent to continuum robots. The complex relationships among mechanical and material nonlinearities, robot motion states, and contact forces are formulated as an optimization problem to reduce the perception complexity. Finally, experimental validation demonstrates the effectiveness of the proposed method. This work lays the foundation for developing safer and smarter continuum robots, enabling broader clinical adoption in complex environments.
Gang Zhang, Junyan Yan, Jibiao Chen +1
Department of Mechanical and Automation Engineering and T Stone Robotics Institute, The Chinese University of Hong Kong, Hong Kong · Curve Robotics Limited, Hong Kong · Shun Hing Institute of Advanced Engineering and Multi-Scale Medical Robotics Center, Hong Kong
Continuum robots offer key advantages in navigating unstructured environments, but their safe operation requires accurate estimation of the external contact forces acting anywhere along the robot body. Estimating these forces at unknown locations is an ill-conditioned problem, particularly for multiple contacts. We propose a unified shape and force estimation framework formulated on a factor graph. By incorporating a Gaussian mixture force parameterization into a discretized probabilistic Cosserat rod model, we reduce the dimensionality of the unknown external forces and mitigate the ill-conditioning of node-wise force estimation. The framework fuses strain, tendon tension, and pose measurements to simultaneously estimate the robot's shape and external forces while accounting for modeling and sensor uncertainties. Numerical simulations demonstrate that the proposed method outperforms existing methods in terms of force location and magnitude estimation for both single and multi-contact scenarios. We further present a progressive variant that introduces basis functions on demand to estimate contact forces sequentially during a simulated confined-navigation task.
Aditya Prakash, Panagiotis Tsiotras
School of Aerospace Engineering Georgia Institute of Technology Atlanta, GA, USA
Hybrid force/motion control requires knowledge of the interaction force and of a task frame defining the force- and motion-controlled directions. These quantities are usually obtained from force/torque sensing or model-based residuals, often assuming also a nominal environment model. This work addresses the online estimation of the contact force and a possibly time-varying task frame using only soft optical tactile sensing, under the assumption of locally planar contact with a negligible contact moment. The proposed method maps a single image of the deformed elastomer of a soft optical tactile sensor to observable contact variables: indentation depth, two surface-to-sensor tilt angles, and 3D contact force, each with a per-sample uncertainty estimate. The mapping is learned through a self-labeling acquisition procedure, in which a manipulator imposes controlled contacts while an auxiliary Force/Torque sensor is used offline to provide ground-truth labels. The tactile measurement is then fused with robot proprioceptive data in an Extended Kalman Filter, producing a continuously updated estimate of the contact task frame and of the interaction force. Control experiments with a DigiTac sensor mounted on a UR10 manipulator demonstrate closed-loop contact force regulation against a flat rigid board in linear and angular motion by a human operator, with touch as the only exteroceptive feedback.
Antonio Rapuano, Simone Orelli, Barbara Bazzana +2
Department of Computer, Control and Management Engineering, Sapienza University of Rome, 00185 Rome, Italy. · Robotics and Mechatronics Department, Faculty of Electrical Engineering, Mathematics and Computer Science, University of Twente, 7500 AE Enschede, The Netherlands.