Resistor-Based Shape Sensor for a Spatial Flexible Manifold

Medina, Oded; Shapiro, Amir; Shvalb, Nir

doi:10.1109/jsen.2016.2619760

Cited by 15 publications

(9 citation statements)

References 14 publications

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“…Resistor shape sensor e resistive sensor is sensitive to twisting or elongation and changes its resistance proportional to the deflection [237,240].…”

Section: Image Cameramentioning

confidence: 99%

“…e information regarding these parameters can be obtained using various sensors inbuilt in the system, which can hugely assist in the robotic control to achieve the defined task. Although the control techniques are out of the scope of this survey, we briefly mention different kinds of sensors [222][223][224][225][226][227][228][229][230][231][232][233][234][235][236][237][238][239][240] used in the current research scenario. Sensors in the field of soft robots are broadly classified into three categories based on their application, i.e., force actuation, shape, and end-effector position estimation.…”

Section: Additional Design Elementsmentioning

confidence: 99%

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Continuum Robots for Manipulation Applications: A Survey

Kolachalama

Lakshmanan

2020

Journal of Robotics

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This paper presents a literature survey documenting the evolution of continuum robots over the past two decades (1999–present). Attention is paid to bioinspired soft robots with respect to the following three design parameters: structure, materials, and actuation. Using this three-faced prism, we identify the uniqueness and novelty of robots that have hitherto not been publicly disclosed. The motivation for this study comes from the fact that continuum soft robots can make inroads in industrial manufacturing, and their adoption will be accelerated if their key advantages over counterparts with rigid links are clear. Four different taxonomies of continuum robots are included in this study, enabling researchers to quickly identify robots of relevance to their studies. The kinematics and dynamics of these robots are not covered, nor is their application in surgical manipulation.

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“…Resistor shape sensor e resistive sensor is sensitive to twisting or elongation and changes its resistance proportional to the deflection [237,240].…”

Section: Image Cameramentioning

confidence: 99%

Section: Additional Design Elementsmentioning

confidence: 99%

Continuum Robots for Manipulation Applications: A Survey

Kolachalama

Lakshmanan

2020

Journal of Robotics

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“…Tanskanen et al [12] have proposed a complete on-device 3D reconstruction pipeline for mobile monocular hand-held devices, which generates dense 3D models with an absolute scale on-site while simultaneously supplying the user with real-time interactive feedback. Medina et al [13] suggest a resistor-based 2D shape sensor, and Shvalb et al [3] show that, using a robotic flexible subreflector, even relatively significant changes in a dish surface can be fixed; naturally, having a 3D model of the current surface of the main dish antenna can improve the accuracy and the run time of such systems.…”

Section: Related Workmentioning

confidence: 99%

Accurate 3D Mapping Algorithm for Flexible Antennas

Asaly

Ben‐Moshe

Shvalb

2018

International Journal of Antennas and Propagation

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This work addresses the problem of performing an accurate 3D mapping of a flexible antenna surface. Consider a high-gain satellite flexible antenna; even a submillimeter change in the antenna surface may lead to a considerable loss in the antenna gain. Using a robotic subreflector, such changes can be compensated for. Yet, in order to perform such tuning, an accurate 3D mapping of the main antenna is required. This paper presents a general method for performing an accurate 3D mapping of marked surfaces such as satellite dish antennas. Motivated by the novel technology for nanosatellites with flexible high-gain antennas, we propose a new accurate mapping framework which requires a small-sized monocamera and known patterns on the antenna surface. The experimental result shows that the presented mapping method can detect changes up to 0.1millimeter accuracy, while the camera is located 1 meter away from the dish, allowing an RF antenna optimization for Ka and Ku frequencies. Such optimization process can improve the gain of the flexible antennas and allow an adaptive beam shaping. The presented method is currently being implemented on a nanosatellite which is scheduled to be launched at the end of 2018.

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“…Reconstruction problems are of interest to the research community due to their importance in engineering applications. These have been studied with regard to heat source identification [1][2][3][4][5], shape reconstruction [6], wave source identification [7][8][9], inverse boundary conditions reconstruction [10,11], and the problem of force identification on plates [12][13][14][15][16][17]. A notable example is [14] by Chierichetti and Ruzzene which uses the confluence algorithm to numerically reconstruct the dynamic response field of the structure from a limited number of experimental measurements (for a survey on analytic methods, see [18]).…”

Section: Introductionmentioning

confidence: 99%

“…(ii) A sensor may also be applied when simultaneous quantities are to be detected. Examples include scalar quantities like detecting all joint angles of a robotic arm [22], extracting the tensile stress field [6,23], and measuring the motion of the sea bed [24]. Examples for simultaneous vector quantities to be detected include stress tensor field extraction and detecting the position of multiple light sources [25] Using multiple sensors will not always detect simultaneous quantities.…”

Section: Introductionmentioning

confidence: 99%

Synthetic Sensor Array Training Sets for Neural Networks

2019

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It is often hard to relate the sensor’s electrical output to the physical scenario when a multidimensional measurement is of interest. An artificial neural network may be a solution. Nevertheless, if the training data set is extracted from a real experimental setup, it can become unreachable in terms of time resources. The same issue arises when the physical measurement is expected to extend across a wide range of values. This paper presents a novel method for overcoming the long training time in a physical experiment set up by bootstrapping a relatively small data set for generating a synthetic data set which can be used for training an artificial neural network. Such a method can be applied to various measurement systems that yield sensor output which combines simultaneous occurrences or wide-range values of physical phenomena of interest. We discuss to which systems our method may be applied. We exemplify our results on three study cases: a seismic sensor array, a linear array of strain gauges, and an optical sensor array. We present the experimental process, its results, and the resulting accuracies.

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Resistor-Based Shape Sensor for a Spatial Flexible Manifold

Cited by 15 publications

References 14 publications

Continuum Robots for Manipulation Applications: A Survey

Continuum Robots for Manipulation Applications: A Survey

Accurate 3D Mapping Algorithm for Flexible Antennas

Synthetic Sensor Array Training Sets for Neural Networks

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