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. 2023:2023:10.1109/sensors56945.2023.10324929.
doi: 10.1109/sensors56945.2023.10324929. Epub 2023 Nov 28.

Data-driven Shape Sensing of Continuum Dexterous Manipulators Using Embedded Capacitive Sensor

Qihang Li  1 , Wenpeng Wang  1 , Joshua Liu  1 , Amit Jain  1   2 , Mehran Armand  1   2
Affiliations

Data-driven Shape Sensing of Continuum Dexterous Manipulators Using Embedded Capacitive Sensor

Qihang Li et al. Proc IEEE Sens. 2023.

Abstract

We propose a novel inexpensive embedded capacitive sensor (ECS) for sensing the shape of Continuum Dexterous Manipulators (CDMs). Our approach addresses some limitations associated with the prevalent Fiber Bragg Grating (FBG) sensors, such as temperature sensitivity and high production costs. ECSs are calibrated using a vision-based system. The calibration of the ECS is performed by a recurrent neural network that uses the kinematic data collected from the vision-based system along with the uncalibrated data from ECSs. We evaluated the performance on a 3D printed prototype of a cable-driven CDM with multiple markers along its length. Using data from three ECSs along the length of the CDM, we computed the angle and position of its tip with respect to its base and compared the results to the measurements of the visual-based system. We found a 6.6% tip position error normalized to the length of the CDM. The work shows the early feasibility of using ECSs for shape sensing and feedback control of CDMs and discusses potential future improvements.

Keywords: capacitive sensing; continuum dexterous manipulators; shape estimation.

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Figures

Fig. 1.
Fig. 1.
Fabrication of sensor structures for capacitive shape sensing in CDMs. (a) The Signal layers serve as primary components for ascertaining both the direction and degree of bending. (b) Depiction of the relative positions of copper plates within the sensor, functioning as carriers of signal and ground. This subfigure illustrates the specific sensor locations where the strips establish their connections.
Fig. 2.
Fig. 2.
The CDM pose tracking and calibration pipeline: (a) RGB image captured, with the orientation and ratio calibrated by mapping the four red points to their respective positions. (b) CDM joint position tracking achieved using an HSV color filter. (c) Collection of capacitance readings from the capacitors. (d) Plot showing the relationship between capacitance values and tip bending angles. The capacitance gap indicates a dependence on previous CDM states. (e) Structure of the RNN (Recurrent Neural Network) with a sequence length of 10. (f) Result of shape regeneration using the RNN output values.
Fig. 3.
Fig. 3.
Training and validation error plotted against epochs.
See this image and copyright information in PMC

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