Dataset Information

Targeted Feature Recognition Using Mechanical Spatial Filtering with a Low-Cost Compliant Strain Sensor.

ABSTRACT: A tactile sensing architecture is presented for detection of surface features that have a particular target size, and the concept is demonstrated with a braille pattern. The approach is akin to an inverse of mechanical profilometry. The sensing structure is constructed by suspending a stretchable strain-sensing membrane over a cavity. The structure is moved over the surface, and a signal is generated through mechanical spatial filtering if a feature is small enough to penetrate into the cavity. This simple design is tailorable and can be realized by standard machining or 3D printing. Images of target features can be produced with even a low-cost compliant sensor. In this work a disposable elastomeric piezoresistive strain sensor was used over a cylindrical "finger" part with a groove having a width corresponding to the braille dot size. A model was developed to help understand the working principle and guide finger design, revealing amplification when the cavity matches the feature size. The new sensing concept has the advantages of being easily reconfigured for a variety of sensing problems and retrofitted to a wide range of robotic hands, as well as compatibility with many compliant sensor types.

SUBMITTER: Barnett EM

PROVIDER: S-EPMC5506026 | biostudies-literature | 2017 Jul

REPOSITORIES: biostudies-literature

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Targeted Feature Recognition Using Mechanical Spatial Filtering with a Low-Cost Compliant Strain Sensor.

Barnett Eli M EM Lofton Julian J JJ Yu Miao M Bruck Hugh A HA Smela Elisabeth E

Scientific reports 20170711 1

A tactile sensing architecture is presented for detection of surface features that have a particular target size, and the concept is demonstrated with a braille pattern. The approach is akin to an inverse of mechanical profilometry. The sensing structure is constructed by suspending a stretchable strain-sensing membrane over a cavity. The structure is moved over the surface, and a signal is generated through mechanical spatial filtering if a feature is small enough to penetrate into the cavity. ...[more]

PMID: 28698623

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Project description:BackgroundCells sense chemical spatial gradients and respond by polarizing internal components. This process can be disrupted by gradient noise caused by fluctuations in chemical concentration.ResultsWe investigated how external gradient noise affects spatial sensing and response focusing on noise-filtering and the resultant tradeoffs. First, using a coarse-grained mathematical model of gradient-sensing and cell polarity, we characterized three negative consequences of noise: Inhibition of the extent of polarization, degradation of directional accuracy, and production of a noisy output polarization. Next, we explored filtering strategies and discovered that a combination of positive feedback, multiple signaling stages, and time-averaging produced good results. There was an important tradeoff, however, because filtering resulted in slower polarization. Simulations demonstrated that a two-stage filter-amplifier resulted in a balanced outcome. Then, we analyzed the effect of noise on a mechanistic model of yeast cell polarization in response to gradients of mating pheromone. This analysis showed that yeast cells likely also combine the above three filtering mechanisms into a filter-amplifier structure to achieve impressive spatial-noise tolerance, but with the consequence of a slow response time. Further investigation of the amplifier architecture revealed two positive feedback loops, a fast inner and a slow outer, both of which contributed to noise-tolerant polarization. This model also made specific predictions about how orientation performance depended upon the ratio between the gradient slope (signal) and the noise variance. To test these predictions, we performed microfluidics experiments measuring the ability of yeast cells to orient to shallow gradients of mating pheromone. The results of these experiments agreed well with the modeling predictions, demonstrating that yeast cells can sense gradients shallower than 0.1% ?m-1, approximately a single receptor-ligand molecule difference between front and back, on par with motile eukaryotic cells.ConclusionsSpatial noise impedes the extent, accuracy, and smoothness of cell polarization. A combined filtering strategy implemented by a filter-amplifier architecture with slow dynamics was effective. Modeling and experimental data suggest that yeast cells employ these elaborate mechanisms to filter gradient noise resulting in a slow but relatively accurate polarization response.

Dataset Information

Targeted Feature Recognition Using Mechanical Spatial Filtering with a Low-Cost Compliant Strain Sensor.

Publications

Targeted Feature Recognition Using Mechanical Spatial Filtering with a Low-Cost Compliant Strain Sensor.

Similar Datasets

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