Development of flexible fabric based tactile sensor for closed loop control of soft robotic actuator

Ponraj, Godwin; Kumar, Kirthika Senthil; Thakor, Nitish V.; Yeow, Chen-Hua; Kukreja, Sunil L.; Ren, Hongliang

doi:10.1109/coase.2017.8256308

Cited by 18 publications

(15 citation statements)

References 33 publications

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“…Further inaccuracies are (i) The subplot (1, 2) is the test result for the KNN algorithm with PSI, where subplot (x; y) means that the subploy at row x and column y, and "ok" is recognized as "four" (44.08 percent) because the index finger is not clearly identification. (ii) The subplot (1,3) gives the test results for the KNN algorithm with OSI, where "six" is recognized as "M. C." (88.2 percent) because the thumb and little fingers are not sufficiently unbent. (iii) The subplot (3,3) displays the test results for the LR algorithm with OSI, where "M. C." is recognized as "good" (76.05 percent) because the thumb is not sufficiently bent.…”

Section: Recognition Results (1) Slasmentioning

confidence: 99%

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Machine learning-based multi-modal information perception for soft robotic hands

Huang

Lin

et al. 2020

Tinshhua Sci. Technol.

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This paper focuses on multi-modal Information Perception (IP) for Soft Robotic Hands (SRHs) using Machine Learning (ML) algorithms. A flexible Optical Fiber-based Curvature Sensor (OFCS) is fabricated, consisting of a Light-Emitting Diode (LED), photosensitive detector, and optical fiber. Bending the roughened optical fiber generates lower light intensity, which reflecting the curvature of the soft finger. Together with the curvature and pressure information, multi-modal IP is performed to improve the recognition accuracy. Recognitions of gesture, object shape, size, and weight are implemented with multiple ML approaches, including the Supervised Learning Algorithms (SLAs) of K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Logistic Regression (LR), and the unSupervised Learning Algorithm (un-SLA) of K-Means Clustering (KMC). Moreover, Optical Sensor Information (OSI), Pressure Sensor Information (PSI), and Double-Sensor Information (DSI) are adopted to compare the recognition accuracies. The experiment results demonstrate that the proposed sensors and recognition approaches are feasible and effective. The recognition accuracies obtained using the above ML algorithms and three modes of sensor information are higer than 85 percent for almost all combinations. Moreover, DSI is more accurate when compared to single modal sensor information and the KNN algorithm with a DSI outperforms the other combinations in recognition accuracy.

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Section: Recognition Results (1) Slasmentioning

confidence: 99%

“…Soft Robotic Hands (SRHs) are presently of interest to many scientists, as they have a number of advantages over traditional hard hands [1,2] . Deimel et Haiming Huang, Junhao Lin, Linyuan Wu, and Fuchun Sun are with the College of Electronics and Information Engineering, Shenzhen University, Shenzhen 518060, China.…”

Section: Introductionmentioning

confidence: 99%

Machine learning-based multi-modal information perception for soft robotic hands

Huang

Lin

et al. 2020

Tinshhua Sci. Technol.

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“…The advancement in additive manufacturing and development of material science has kept up with the innovation in robotics, wearable electronics [1], epidermal electronic systems [2], human-machine interfaces [3], soft robotics [4], other biomedical devices [5, 6], and the related systems [7]. In most of these systems, its sensory feedback plays an important role in contemplating the efficiency and performance accuracy.…”

Section: Introductionmentioning

confidence: 99%

A Review of Printable Flexible and Stretchable Tactile Sensors

2019

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Flexible and stretchable tactile sensors that are printable, nonplanar, and dynamically morphing are emerging to enable proprioceptive interactions with the unstructured surrounding environment. Owing to its varied range of applications in the field of wearable electronics, soft robotics, human-machine interaction, and biomedical devices, it is required of these sensors to be flexible and stretchable conforming to the arbitrary surfaces of their stiff counterparts. The challenges in maintaining the fundamental features of these sensors, such as flexibility, sensitivity, repeatability, linearity, and durability, are tackled by the progress in the fabrication techniques and customization of the material properties. This review is aimed at summarizing the recent progress of rapid prototyping of sensors, printable material preparation, required printing properties, flexible and stretchable mechanisms, and promising applications and highlights challenges and opportunities in this research paradigm.

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“…In the same context, various studies have been performed to enable these flexible tactile sensors to sense external inputs such as proximity [4,5], strain [6,7], and pressure [8,9]. Among these sensors, force sensors have been widely used in the field of robotics to realize functions such as object classification [10] and robot manipulation with a feedback system [11,12]. During the time of force sensing, it is very important to detect shear force as well as normal force.…”

Section: Introductionmentioning

confidence: 99%

Flexible Shear and Normal Force Sensor Using only One Layer of Polyvinylidene Fluoride Film

Lee

Chung

et al. 2019

Applied Sciences

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We have proposed a flexible sensor that can sense shear and normal forces, and can be fabricated through a simple process using only one layer of polyvinylidene fluoride (PVDF) film. For the measurement of shear and normal forces, one layer of PVDF film was sealed in a three-dimensionally structured polydimethylsiloxane (PDMS). In the structure, the sensor produced voltage signals corresponding to the shear and normal forces. Using this property, we aimed to demonstrate how to sense the magnitude and direction of the force applied to the sensor from its output voltages. Furthermore, the proposed sensor with a 2 × 2 array was able to measure the applied force in real time.

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Development of flexible fabric based tactile sensor for closed loop control of soft robotic actuator

Cited by 18 publications

References 33 publications

Machine learning-based multi-modal information perception for soft robotic hands

Machine learning-based multi-modal information perception for soft robotic hands

A Review of Printable Flexible and Stretchable Tactile Sensors

Flexible Shear and Normal Force Sensor Using only One Layer of Polyvinylidene Fluoride Film

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