Robotic grasp control with high-resolution combined tactile and proximity sensing

Shimonomura, Kazuhiro; Nakashima, Hiroto; Nozu, Kentaro

doi:10.1109/icra.2016.7487126

Cited by 32 publications

(20 citation statements)

References 17 publications

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“…Using infrared light as the illumination light makes it possible to separate the incident light to the camera into the visible light from outside the light conductive plate and the infrared light occurring by contact with the wavelength (λ in Equation (3)). The combined contact and proximity sensor in [25,50,51] uses this method for contact detection. Moreover, this method to detect contact using the light conductive plate has been used for multi-touch detection sensors in the field of human–computer interfaces [52,53].…”

Section: Physical Contact To Light Conversionmentioning

confidence: 99%

“…The direct method does not use the elastomer cover, and the object directly contacts the light conducting plate. Figure 3 shows the example of a tactile image sensor with the direct light conductive plate-based method [25]. The contact area appears with single-pixel resolution, and the shape of the contact object can be seen clearly.…”

Section: Physical Contact To Light Conversionmentioning

confidence: 99%

“…In [25,51], a compact and thin multiple camera system, called a “compound-eye camera”, [103,104] was used (see Figure 3a). This consisted of a lens array, a signal separator, and an image sensor.…”

Section: Shape Of Tactile Skin and Sensor Sizementioning

confidence: 99%

“…It was constructed as an array of elemental parts called “units” that included a lens and a small segment of the image sensor. The compound-eye camera used in [25] had an array of nine units. Such a development of the optical system is also useful for reducing the size and thickness of tactile image sensors.…”

Section: Shape Of Tactile Skin and Sensor Sizementioning

confidence: 99%

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Tactile Image Sensors Employing Camera: A Review

Shimonomura

2019

Sensors

Self Cite

134

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A tactile image sensor employing a camera is capable of obtaining rich tactile information through image sequences with high spatial resolution. There have been many studies on the tactile image sensors from more than 30 years ago, and, recently, they have been applied in the field of robotics. Tactile image sensors can be classified into three typical categories according to the method of conversion from physical contact to light signals: Light conductive plate-based, marker displacement- based, and reflective membrane-based sensors. Other important elements of the sensor, such as the optical system, image sensor, and post-image analysis algorithm, have been developed. In this work, the literature is surveyed, and an overview of tactile image sensors employing a camera is provided with a focus on the sensing principle, typical design, and variation in the sensor configuration.

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Section: Physical Contact To Light Conversionmentioning

confidence: 99%

Section: Physical Contact To Light Conversionmentioning

confidence: 99%

Section: Shape Of Tactile Skin and Sensor Sizementioning

confidence: 99%

Section: Shape Of Tactile Skin and Sensor Sizementioning

confidence: 99%

See 2 more Smart Citations

Tactile Image Sensors Employing Camera: A Review

Shimonomura

2019

Sensors

Self Cite

134

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“…Three main techniques are developed to acquire information from the contact area: (i) Light- conductive plate; (ii) Reflective membrane; (iii) Marker-based membrane. Light-conductive tactile sensors emit light to the contact area and the camera observers light intensity changes to acquire contact position and measure force [ 14 ]. Reflective membrane sensors consider a reflective flexible material to observe changes in the shape of membrane which enable the sensor to measure orientation as well as texture of the object [ 15 ].…”

Section: Related Workmentioning

confidence: 99%

Dynamic-Vision-Based Force Measurements Using Convolutional Recurrent Neural Networks

Naeini

Makris

Gan

et al. 2020

Sensors

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In this paper, a novel dynamic Vision-Based Measurement method is proposed to measure contact force independent of the object sizes. A neuromorphic camera (Dynamic Vision Sensor) is utilizused to observe intensity changes within the silicone membrane where the object is in contact. Three deep Long Short-Term Memory neural networks combined with convolutional layers are developed and implemented to estimate the contact force from intensity changes over time. Thirty-five experiments are conducted using three objects with different sizes to validate the proposed approach. We demonstrate that the networks with memory gates are robust against variable contact sizes as the networks learn object sizes in the early stage of a grasp. Moreover, spatial and temporal features enable the sensor to estimate the contact force every 10 ms accurately. The results are promising with Mean Squared Error of less than 0.1 N for grasping and holding contact force using leave-one-out cross-validation method.

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Recent Progress in Advanced Tactile Sensing Technologies for Soft Grippers

Qu,

Mao,

et al. 2023

Adv Funct Materials

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Tactile sensing technology is crucial for soft grippers. Soft grippers equipped with intelligent tactile sensing systems based on various sensors can interact safely with the unstructured environments and obtain precise properties of objects (e.g., size and shape). It is essential to develop state‐of‐the‐art sensing technologies for soft grippers to handle different grasping tasks. In this review, the development of tactile sensing techniques for robotic hands is first introduced. Then, the principles and structures of different types of sensors normally adopted in soft grippers, including capacitive tactile sensors, piezoresistive tactile sensors, piezoelectric tactile sensors, fiber Bragg grating (FBG) sensors, vision‐based tactile sensors, triboelectric tactile sensors, and other advanced sensors developed recently are briefly presented. Furthermore, sensing modalities and methodologies for soft grippers are also described in aspects of force measurement, perception of object properties, slip detection, and fusion of perception. The application scenarios of soft grippers are also summarized based on these advanced sensing technologies. Finally, the challenges of tactile sensing technologies for soft grippers that need to be tackled are discussed and perspectives in addressing these challenges are pointed out.

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Robotic grasp control with high-resolution combined tactile and proximity sensing

Cited by 32 publications

References 17 publications

Tactile Image Sensors Employing Camera: A Review

Tactile Image Sensors Employing Camera: A Review

Dynamic-Vision-Based Force Measurements Using Convolutional Recurrent Neural Networks

Recent Progress in Advanced Tactile Sensing Technologies for Soft Grippers

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