Distributed sensing: multiple capacitive stretch sensors on a single channel

Tairych, Andreas; Anderson, Iain A.

doi:10.1117/12.2260416

Cited by 4 publications

(11 citation statements)

References 5 publications

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“…presented a technique for multi‐actuator sensing on a single physical channel, illustrating paths for future research in this area. [ 21 ]…”

Section: Discussionmentioning

confidence: 99%

“…Ly et al presented miniaturized capacitive self-sensing circuitry that could be integrated into the system [15] and Tairych et al presented a technique for multiactuator sensing on a single physical channel, illustrating paths for future research in this area. [21] Tradeoffs between safe current limits and fast actuation speeds must be considered when designing electrohydraulic actuators and corresponding high-voltage driving electronics. The exposed electrodes of the actuators could result in electrical discharge through a human operator [22] and electrical breakdown or corona discharge could serve as an ignition source.…”

Section: Integrated High-voltage Driving Electronicsmentioning

confidence: 99%

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A Soft, Fast and Versatile Electrohydraulic Gripper with Capacitive Object Size Detection

Yoder

Macari

Kleinwaks

et al. 2022

Adv Funct Materials

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Soft robotic grippers achieve increased versatility and reduced complexity through intelligence embodied in their flexible and conformal structures. The most widely used soft grippers are pneumatically driven; they are simple and effective but require bulky air compressors that limit their application space and external sensors or computationally expensive vision systems for pick verification. In this study, a multi‐material architecture for self‐sensing electrohydraulic bending actuators is presented that enables a new class of highly versatile and reconfigurable soft grippers that are electrically driven and feature capacitive pick verification and object size detection. These electrohydraulic grippers are fast (step input results in finger closure in 50 ms), draw low power (6.5 mW per finger to hold grasp), and can pick a wide variety of objects with simple binary electrical control. Integrated high‐voltage driving electronics are presented that greatly increase the application space of the grippers and make them readily compatible with commercially available robotic arms.

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“…presented a technique for multi‐actuator sensing on a single physical channel, illustrating paths for future research in this area. [ 21 ]…”

Section: Discussionmentioning

confidence: 99%

Section: Integrated High-voltage Driving Electronicsmentioning

confidence: 99%

A Soft, Fast and Versatile Electrohydraulic Gripper with Capacitive Object Size Detection

Yoder

Macari

Kleinwaks

et al. 2022

Adv Funct Materials

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“…Nevertheless, capacitive sensors have been successfully made using conductive silicone for measuring pressure and shear stresses simultaneously at the stump-socket interface of lower-limb amputees (Laszczak et al, 2015(Laszczak et al, , 2016. Conductive fabrics have been used by Andreas Tairych (2017) to create multiple capacitive stretch sensors requiring only one channel for measurement; Atalay et al (2017) used conductive stretch fabric as the electrodes sandwiching a silicone dielectric for a customizable strain sensor for human motion tracking; Kappel et al (2012) developed a strain sensor based on a dielectric electro-active polymer (DEAP) that acts as an elastic capacitive material, strainable in one direction for measuring in-shoe navicular drop during gait; Zens et al (2015) used a complex layering of non-conductive PDMS and conductive PDMS made using carbon black particles as a novel approach to dynamic knee laxity measurement (Fassler and Majidi, 2013;Zens et al, 2015) produced soft-matter capacitors and inductors from microchannels of liquid-phase gallium-indium-tin alloy (galinstan) embedded in Ecoflex 00-30.…”

Section: Capacitive Strain Sensingmentioning

confidence: 99%

A Deep Learning Approach to Non-linearity in Wearable Stretch Sensors

et al. 2019

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There is a growing need for flexible stretch sensors to monitor real time stress and strain in wearable technology. However, developing stretch sensors with linear responses is difficult due to viscoelastic and strain rate dependent effects. Instead of trying to engineer the perfect linear sensor we take a deep learning approach which can cope with non-linearity and yet still deliver reliable results. We present a general method for calibrating highly hysteretic resistive stretch sensors. We show results for textile and elastomeric stretch sensors however we believe the method is directly applicable to any physical choice of sensor material and fabrication, and easily adaptable to other sensing methods, such as those based on capacitance. Our algorithm does not require any a priori knowledge of the physical attributes or geometry of the sensor to be calibrated, which is a key advantage as stretchable sensors are generally applicable to highly complex geometries with integrated electronics requiring bespoke manufacture. The method involves three-stages. The first stage requires a calibration step in which the strain of the sensor material is measured using a webcam while the electrical response is measured via a set of arduino-based electronics. During this data collection stage, the strain is applied manually by pulling the sensor over a range of strains and strain rates corresponding to the realistic in-use strain and strain rates. The correlated data between electrical resistance and measured strain and strain rate are stored. In the second stage the data is passed to a Long Short Term Memory Neural Network (LSTM) which is trained using part of the data set. The ability of the LSTM to predict the strain state given a stream of unseen electrical resistance data is then assessed and the maximum errors established. In the third stage the sensor is removed from the webcam calibration set-up and embedded in the wearable application where the live stream of electrical resistance is the only measure of strain-this corresponds to the proposed use case. Highly accurate stretch topology mapping is achieved for the three commercially available flexible sensor materials tested.

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“…These stretchable sensors typically use electrical properties [e.g., resistance (4,13), capacitance (14,15), voltage (16), and current (17)] in planar arrays (or "skins") to localize surface deformations (17)(18)(19)(20)(21). Compared with electricity, light carries information faster and with higher data densities through changes in intensity, phase, polarization, or wavelength (22).…”

Section: Introductionmentioning

confidence: 99%

Optical lace for synthetic afferent neural networks

Mishra

Bai

et al. 2019

Sci. Robot.

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Whereas vision dominates sensing in robots, animals with limited vision deftly navigate their environment using other forms of perception, such as touch. Efforts have been made to apply artificial skins with tactile sensing to robots for similarly sophisticated mobile and manipulative skills. The ability to functionally mimic the afferent sensory neural network, required for distributed sensing and communication networks throughout the body, is still missing. This limitation is partially due to the lack of cointegration of the mechanosensors in the body of the robot. Here, lacings of stretchable optical fibers distributed throughout 3D-printed elastomer frameworks created a cointegrated body, sensing, and communication network. This soft, functional structure could localize deformation with submillimeter positional accuracy (error of 0.71 millimeter) and sub-Newton force resolution (~0.3 newton).

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Distributed sensing: multiple capacitive stretch sensors on a single channel

Cited by 4 publications

References 5 publications

A Soft, Fast and Versatile Electrohydraulic Gripper with Capacitive Object Size Detection

A Soft, Fast and Versatile Electrohydraulic Gripper with Capacitive Object Size Detection

A Deep Learning Approach to Non-linearity in Wearable Stretch Sensors

Optical lace for synthetic afferent neural networks

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