A dual-mode proximity sensor with combination of inductive and capacitive sensing units

Kan, Wenqing; Zhang, Shuai; Zhang, Xiao; Guo, Xiaohui; Liu, Ping

doi:10.1108/sr-06-2017-0111

Cited by 15 publications

(7 citation statements)

References 22 publications

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“…Take the capacitive proximity sensor as an example, when different objects approach the sensor, the change curve of the sensor capacitance is different, for example, in the article [23]. When aluminum, copper, and plastic approach the same capacitance sensor, the relationship between capacitance value and distance is not the same.…”

Section: Processing and Analyzing Of Datamentioning

confidence: 99%

Perspective of Proximity Sensors on Robots Using Cross-Thought

Xing¹

2023

HSET

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Robots play a significantly important role in the area of industry manufacture. In recent years, with the development of intelligence robots, collaborative robots and human-robot interactive robots, proximity sensors applied on robots become increasingly important and popular, since proximity sensors can assure safety during collaboration process between human and robots and help to complete difficult tasks such as objects grasping, objects handover, etc. This article gives the lasted development of proximity sensors and provides an introduction on proximity sensors which are used on robots by their categories. then some concrete applications of the proximity sensors on robots are introduced, such as collision prevention, objects grasping and handover, landing process control of robot legs and so on. In addition, this article also provides some possible future development directions of proximity sensors, such as mixture usage of different proximity sensors, more application scenarios, improvement of data processing method, etc., which can help future researchers to find research directions quickly.

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Section: Processing and Analyzing Of Datamentioning

confidence: 99%

Perspective of Proximity Sensors on Robots Using Cross-Thought

Xing¹

2023

HSET

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“…This diversity in response is attributed to differences in the dielectric constants among materials. [26] When placed near the capacitive e-skin sequentially, the disturbance of the fringe electric field caused by them and the distribution of the electric field lines are different, [27,28] thus allowing for the accurate perception of multiple materials with the help of the mutual capacitance effect, making the capacitive e-skin a powerful tool for inferring material properties. Additionally, precise perception of an object often requires the coordination of multiple attributes, particularly the material species and surface shape types, which are essential elements for perception.…”

Section: Introductionmentioning

confidence: 99%

Micropyramid Array Bimodal Electronic Skin for Intelligent Material and Surface Shape Perception Based on Capacitive Sensing

Niu,

Wei,

et al. 2023

Advanced Science

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Developing electronic skins (e‐skins) that are comparable to or even beyond human tactile perception holds significant importance in advancing the process of intellectualization. In this context, a machine‐learning‐motivated micropyramid array bimodal (MAB) e‐skin based on capacitive sensing is reported, which enables spatial mapping applications based on bimodal sensing (proximity and pressure) implemented via fringing and iontronic effects, such as contactless measurement of 3D objects and contact recognition of Braille letters. Benefiting from the iontronic effect and single‐micropyramid structure, the MAB e‐skin in pressure mode yields impressive features: a maximum sensitivity of 655.3 kPa−1 (below 0.5 kPa), a linear sensitivity of 327.9 kPa−1 (0.5–15 kPa), and an ultralow limit of detection of 0.2 Pa. With the assistance of multilayer perceptron and convolutional neural network, the MAB e‐skin can accurately perceive 6 materials and 10 surface shapes based on the training and learning using the collected datasets from proximity and pressure modes, thus allowing it to achieve the precise perception of different objects within one proximity‐pressure cycle. The development of this MAB e‐skin opens a new avenue for robotic skin and the expansion of advanced applications.

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“…Sensors such as Light Detection and Ranging (lidar) or depth sensor are precise in the optical approach [ 7 ], but they are hard to implement and still suffer from object occlusion. The inductive approach can realize robust sensors, but it only works with conductive objects [ 8 ]. On the other hand, sensors based on the capacitive approach can detect both conductive and non-conductive objects, but they are vulnerable to disturbances and contamination [ 9 , 10 ].…”

Section: Introductionmentioning

confidence: 99%

Tomographic Proximity Imaging Using Conductive Sheet for Object Tracking

Yoshimoto

Yamamoto

2021

Sensors

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This paper proposes a proximity imaging sensor based on a tomographic approach with a low-cost conductive sheet. Particularly, by defining capacitance density, physical proximity information is transformed into electric potential. A novel theoretical model is developed to solve the capacitance density problem using the tomographic approach. Additionally, a prototype is built and tested based on the model, and the system solves an inverse problem for imaging the capacitance density change that indicates the object’s proximity change. In the evaluation test, the prototype reaches an error rate of 10.0–15.8% in horizontal localization at different heights. Finally, a hand-tracking demonstration is carried out, where a position difference of 33.8–46.7 mm between the proposed sensor and depth camera is achieved at 30 fps.

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A dual-mode proximity sensor with combination of inductive and capacitive sensing units

Cited by 15 publications

References 22 publications

Perspective of Proximity Sensors on Robots Using Cross-Thought

Perspective of Proximity Sensors on Robots Using Cross-Thought

Micropyramid Array Bimodal Electronic Skin for Intelligent Material and Surface Shape Perception Based on Capacitive Sensing

Tomographic Proximity Imaging Using Conductive Sheet for Object Tracking

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