Compressed sensing for tactile skins

Hollis, Brayden; Patterson, Stacy; Trinkle, Jeff

doi:10.1109/icra.2016.7487128

Cited by 9 publications

(9 citation statements)

References 34 publications

(64 reference statements)

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“…In addition, the research also needs to deal with challenges related to data acquisition, such as trade-off between the spatial resolution and temporal resolution, simultaneous sensing of multiple stimuli, etc. [ 128 , 129 , 130 , 131 ]. The massive amount of tactile signals need to be acquired at high rates.…”

Section: Intelligent Signal Processing For Smart Tactile Sensingmentioning

confidence: 99%

Novel Tactile Sensor Technology and Smart Tactile Sensing Systems: A Review

Zou

Wang

et al. 2017

Sensors

221

110

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During the last decades, smart tactile sensing systems based on different sensing techniques have been developed due to their high potential in industry and biomedical engineering. However, smart tactile sensing technologies and systems are still in their infancy, as many technological and system issues remain unresolved and require strong interdisciplinary efforts to address them. This paper provides an overview of smart tactile sensing systems, with a focus on signal processing technologies used to interpret the measured information from tactile sensors and/or sensors for other sensory modalities. The tactile sensing transduction and principles, fabrication and structures are also discussed with their merits and demerits. Finally, the challenges that tactile sensing technology needs to overcome are highlighted.

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Section: Intelligent Signal Processing For Smart Tactile Sensingmentioning

confidence: 99%

Novel Tactile Sensor Technology and Smart Tactile Sensing Systems: A Review

Zou

Wang

et al. 2017

Sensors

221

110

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“…In previous work [3], we investigated the application of compressed sensing in planar tactile arrays. For the measurement matrix, we used the Scrambled Block Hadamard Ensemble (SBHE), which was developed by Gan et al [21] for compressed sensing in the image domain.…”

Section: B Application To Tactile Skinsmentioning

confidence: 99%

“…Using these standard (i.e. pre-existing and non-optimized) compressed sensing tools, we achieved 50Hz reconstruction rates for a tactile array of 4096 taxels from 1365 measurements (a compression factor of 3) [3]. This array contains approximately the same number of sensors as the the largest existing tactile system, and the reconstruction rate is on the same order of magnitude as that system's measurement rate [23].…”

Section: B Application To Tactile Skinsmentioning

confidence: 99%

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Compressed Learning for Tactile Object Recognition

Hollis

Patterson

Trinkle

2018

IEEE Robot. Autom. Lett.

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The potential of large tactile arrays to improve robot perception for safe operation in human-dominated environments and of high-resolution tactile arrays to enable humanlevel dexterous manipulation is well accepted. However, the increase in the number of tactile sensing elements introduces challenges including wiring complexity, power consumption, and data processing. To help address these challenges, we previously developed a tactile sensing technique based compressed sensing that reduces hardware complexity and data transmission, while allowing accurate reconstruction of the fullresolution signal. In this paper, we apply tactile compressed sensing to the problem of object classification. Specifically, we perform object classification on the compressed tactile data. We evaluate our method using BubbleTouch, our tactile array simulator. Our results show our approach achieves high classification accuracy, even with compression factors up to 64.

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“…For example, Locality Sensitive Hashing (LSH) is used to deal with the problem of stereo correspondence estimation [13], multimodel fusion techniques are adopted in humanoid robots to process large-scale language data [14], and Local Difference Binary (LDB) descriptor is applied to obtain a robust global image description for place recognition and loop closure detection [15]. In [16] the authors proposed to compress sensory data from tactile skins. Similarly, the distributed sensor data with high-frequencies can be compressed as coresets for streaming motion [17].…”

Section: Introductionmentioning

confidence: 99%

Rhythmic Representations: Learning Periodic Patterns for Scalable Place Recognition at a Sublinear Storage Cost

Jacobson

Milford

2018

IEEE Robot. Autom. Lett.

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Robotic and animal mapping systems share many challenges and characteristics: they must function in a wide variety of environmental conditions, enable the robot or animal to navigate effectively to find food or shelter, and be computationally tractable from both a speed and storage perspective. With regards to map storage, the mammalian brain appears to take a diametrically opposed approach to all current robotic mapping systems. Where robotic mapping systems attempt to solve the data association problem to minimise representational aliasing, neurons in the brain intentionally break data association by encoding large (potentially unlimited) numbers of places with a single neuron. In this paper, we propose a novel method based on supervised learning techniques that seeks out regularly repeating visual patterns in the environment with mutually complementary co-prime frequencies, and an encoding scheme that enables storage requirements to grow sub-linearly with the size of the environment being mapped. To improve robustness in challenging real-world environments while maintaining storage growth sub-linearity, we incorporate both multi-exemplar learning and data augmentation techniques. Using large benchmark robotic mapping datasets, we demonstrate the combined system achieving high-performance place recognition with sub-linear storage requirements, and characterize the performance-storage growth trade-off curve. The work serves as the first robotic mapping system with sub-linear storage scaling properties, as well as the first largescale demonstration in real-world environments of one of the proposed memory benefits of these neurons.

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Compressed sensing for tactile skins

Cited by 9 publications

References 34 publications

Novel Tactile Sensor Technology and Smart Tactile Sensing Systems: A Review

Novel Tactile Sensor Technology and Smart Tactile Sensing Systems: A Review

Compressed Learning for Tactile Object Recognition

Rhythmic Representations: Learning Periodic Patterns for Scalable Place Recognition at a Sublinear Storage Cost

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