Biomimetic tactile sensors and signal processing with spike trains: A review

Yi, Zhengkun; Zhang, Yilei; Peters, Jan

doi:10.1016/j.sna.2017.09.035

Cited by 50 publications

(33 citation statements)

References 96 publications

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“…In the last years, a variety of technologies are being used in order to manufacture tactile sensors: piezo-resistive, capacitive, optical, ultrasonic, based on piezo-electric materials, based on magnetic transduction and based on barometric sensor, among others. Recently, Yi et al [10] presented a review of some of these tactile transduction techniques. The most known for robotic hands and grippers are commented below:…”

Section: Types Of Tactile Sensorsmentioning

confidence: 99%

Tactile-Driven Grasp Stability and Slip Prediction

2019

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One of the challenges in robotic grasping tasks is the problem of detecting whether a grip is stable or not. The lack of stability during a manipulation operation usually causes the slippage of the grasped object due to poor contact forces. Frequently, an unstable grip can be caused by an inadequate pose of the robotic hand or by insufficient contact pressure, or both. The use of tactile data is essential to check such conditions and, therefore, predict the stability of a grasp. In this work, we present and compare different methodologies based on deep learning in order to represent and process tactile data for both stability and slip prediction.

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Section: Types Of Tactile Sensorsmentioning

confidence: 99%

Tactile-Driven Grasp Stability and Slip Prediction

2019

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“…The sense of touch is an important gateway to interact with the environment [1]. Robots and hand prosthesis endowed with the sense of touch can better and more easily manipulate objects, and physically collaborate with other agents.…”

Section: Introductionmentioning

confidence: 99%

“…of Groningen). Affiliation: 1 Connected Objects Sensing Materials Integrated Circuits (COSMIC) lab. University of Genova-DITEN, Genova, Italy.…”

Section: Introductionmentioning

confidence: 99%

Artificial Bio-Inspired Tactile Receptive Fields for Edge Orientation Classification

Dabbous

Mastella

Natarajan

et al. 2021

2021 IEEE International Symposium on Circuits and Systems (ISCAS)

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Robots and users of hand prosthesis could easily manipulate objects if endowed with the sense of touch. Towards this goal, information about touched objects and surfaces has to be inferred from raw data coming from the sensors. An important cue for objects discrimination is the orientation of edges, that is used both in artificial vision and touch as pre-processing stage. We present a spiking neural network, inspired on the encoding of edges in human first order tactile afferents. The network uses three layers of Leaky Integrate and Fire neurons to distinguish different edge orientations of a bar pressed on the artificial skin of the iCub robot. The architecture is successfully able to discriminate eight different orientations (from 0 o to 180 o ), by implementing a structured model of overlapping receptive fields. We demonstrate that the network can learn the appropriate connectivity through unsupervised spike based learning, and that the number and spatial distribution of sensitive areas within the receptive fields are important in edge orientation discrimination.

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“…When tactile sensors are used for robotic or biomedical applications, they are used to ‘feel’ the tactile environment [1,2]. The resulting signals from tactile shear and slip sensors would act as triggers for certain predetermined actions, such as termination of mechanical movement, initiation of an electrical function, or adjustment of applied mechanical pressure.…”

Section: Introductionmentioning

confidence: 99%

Highly Sensitive Tactile Shear Sensor Using Spatially Digitized Contact Electrodes

Choi

Hwang

Yoon

et al. 2019

Sensors

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In this article, we report on a highly sensitive tactile shear sensor that was able to detect minute levels of shear and surface slip. The sensor consists of a suspended elastomer diaphragm with a top ridge structure, a graphene layer underneath, and a bottom substrate with multiple spatially digitized contact electrodes. When shear is applied to the top ridge structure, it creates torque and deflects the elastomer downwards. Then, the graphene electrode makes contact with the bottom spatially digitized electrodes completing a circuit producing output currents depending on the number of electrodes making contact. The tactile shear sensor was able to detect shear forces as small as 6 μN, detect shear direction, and also distinguish surface friction and roughness differences of shearing objects. We also succeeded in detecting the contact slip motion of a single thread demonstrating possible applications in future robotic fingers and remote surgical tools.

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Biomimetic tactile sensors and signal processing with spike trains: A review

Cited by 50 publications

References 96 publications

Tactile-Driven Grasp Stability and Slip Prediction

Tactile-Driven Grasp Stability and Slip Prediction

Artificial Bio-Inspired Tactile Receptive Fields for Edge Orientation Classification

Highly Sensitive Tactile Shear Sensor Using Spatially Digitized Contact Electrodes

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