Abstract
This work shows the development and characterization of a fiber optic tactile sensor based on Fiber Bragg Grating (FBG) technology. The sensor is a 3×3 array of FBGs encapsulated in a PDMS compliant polymer. The strain experienced by each FBG is transduced into a Bragg wavelength shift and the inverse characteristics of the sensor were computed by means of a feedforward neural network. A 21 mN RMSE error was achieved in estimating the force over the 8 N experimented load range while including all probing sites in the neural network training procedure, whereas the median force RMSE was 199 mN across the 200 instances of a Monte Carlo randomized selection of experimental sessions to evaluate the calibration under generalized probing conditions. The static metrological properties and the possibility to fabricate sensors with relatively high spatial resolution make the proposed design attractive for the sensorization of robotic hands. Furthermore, the proved MRI-compatibility of the sensor opens other application scenarios, such as the possibility to employ the array for force measurement during functional MRI-measured brain activation.
Highlights
Tactile sensors transduce quantities, such as force, pressure, temperature, vibration, and slip, through the physical interaction with the object.The first interest in touch-sensing technology arose between the end of the seventies and the beginning of the eighties, when researchers started investigating its application in the field of robotics [1, 2]
When evaluating the neural network according to the first procedure described in Section 3.2, a 21 mN root mean squared error (RMSE) was achieved in the validation set (433 mN2 Mean Squared Error (MSE), Figure 7), without offset in the error distribution (Figure 8) and with high regression quality as confirmed by the R coefficients being very close to 1 for the calibration, validation, and test data sets and for all datasets grouped together (Figure 9)
When evaluating the neural network according to the second, more challenging, procedure described in Section 3.2, the 25th, 50th, and 75th percentile of RMSE of the estimated force were 124 mN, 199 mN, and 354 mN, respectively, across 200 instances of the randomized selection of experimental sessions (Figure 10), whereas the 25th, 50th, and 75th percentile of the mean error of the estimated force were −38 mN, 3 mN, and 34 mN, respectively (Figure 11)
Summary
Tactile sensors transduce quantities, such as force, pressure, temperature, vibration, and slip, through the physical interaction with the object. A number of technologies were investigated for the development of tactile sensors, including principles of sensing such as piezoresistivity, piezoelectricity, and capacitance change; anyway a set of requirements independent of the technology can be identified for tactile sensors for force and pressure measurement inspired for the human tactile system, for example, the capability of detecting both static and dynamic forces, high spatial resolution and small size, compliant sensing surface, low hysteresis, high repeatability, and low discrimination threshold All these features should be owned by the sensing system in compliance with the specific purpose [10] and be reflected in the calibration method, either with model-based approaches [11, 12] or with neural networks [13, 14]. The FBG-based array was scanned with 1.5 T MRI
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