scholarly journals Tactile Sensors for Friction Estimation and Incipient Slip Detection—Toward Dexterous Robotic Manipulation: A Review

2018 ◽  
Vol 18 (22) ◽  
pp. 9049-9064 ◽  
Author(s):  
Wei Chen ◽  
Heba Khamis ◽  
Ingvars Birznieks ◽  
Nathan F. Lepora ◽  
Stephen J. Redmond
Keyword(s):  
Robotic Manipulation ◽  
Tactile Sensors ◽  
Friction Estimation ◽  
Slip Detection

A New Highly Sensitive Dielectric Slip Detection Sensor for a Robotic Operation

2017 ◽  
Author(s):  
Sung Joon Kim ◽  
Ja Choon Koo
Keyword(s):  
Tactile Sensor ◽  
Relative Displacement ◽  
Large Area ◽  
Tactile Sensors ◽  
Post Process ◽  
Highly Sensitive ◽  
Grasping And Manipulation ◽  
Working Principle ◽  
Slip Detection

For dexterous grasping and manipulation, tactile sensors recognizing contact object are essential. Electronic skin (E-skin) with tactile sensors plays a role as both receiving information for grasping and protecting robot frame. This paper presents a polymer tactile sensor covering large area to fulfill role of E-skin. The sensor has a thin air gap between polymer layers and it is deformed reacting slip input. When slip is occurred, there is relative displacement between surrounding layer and it incurs change of electrode separation. NBR is used to sensor substrate because of its tough and flexible characteristic. Ultrathin aluminum tape is employed for electrodes. There is a changeability of size of the sensor because of its simple but effective working principle and structure. Slip detecting algorithm doesn’t have a post process such as FFT or DWT, so there isn’t delay for processing time. It realizes real-time slip detection reducing reaction time of robot hand.

scholarly journals Multimodal grasp data set: A novel visual–tactile data set for robotic manipulation

2019 ◽  
Vol 16 (1) ◽  
pp. 172988141882157 ◽  
Author(s):  
Tao Wang ◽  
Chao Yang ◽  
Frank Kirchner ◽  
Peng Du ◽  
Fuchun Sun ◽  
...  
Keyword(s):  
Short Term Memory ◽  
Robotic Manipulation ◽  
Robot Hand ◽  
Quality Of Data ◽  
Short Term ◽  
Data Set ◽  
Term Memory ◽  
Generalization Ability ◽  
Long Short Term Memory ◽  
Slip Detection

This article introduces a visual–tactile multimodal grasp data set, aiming to further the research on robotic manipulation. The data set was built by the novel designed dexterous robot hand, the Intel’s Eagle Shoal robot hand (Intel Labs China, Beijing, China). The data set contains 2550 sets data, including tactile, joint, time label, image, and RGB and depth video. With the integration of visual and tactile data, researchers could be able to better understand the grasping process and analyze the deeper grasping issues. In this article, the building process of the data set was introduced, as well as the data set composition. In order to evaluate the quality of data set, the tactile data were analyzed by short-time Fourier transform. The tactile data–based slip detection was realized by long short-term memory and contrasted with visual data. The experiments compared the long short-term memory with the traditional classifiers, and generalization ability on different grasp directions and different objects is implemented. The results have proved that the data set’s value in promoting research on robotic manipulation area showed the effective slip detection and generalization ability of long short-term memory. Further work on visual and tactile data will be devoted to in the future.

Development of Direct-printed Tactile Sensors for Gripper Control through Contact and Slip Detection

2018 ◽  
Vol 16 (2) ◽  
pp. 929-936 ◽  
Author(s):  
Ju-Kyoung Lee ◽  
Hyun-Hee Kim ◽  
Jae-Won Choi ◽  
Kyung-Chang Lee ◽  
Suk Lee
Keyword(s):  
Tactile Sensors ◽  
Slip Detection

Tactile Behaviors with the Vision-Based Tactile Sensor FingerVision

2019 ◽  
Vol 16 (03) ◽  
pp. 1940002 ◽  
Author(s):  
Akihiko Yamaguchi ◽  
Christopher G. Atkeson
Keyword(s):  
Computer Vision ◽  
High Resolution ◽  
Contact Force ◽  
Low Cost ◽  
Tactile Sensor ◽  
Tactile Sensors ◽  
Elastic Skin ◽  
Slip Detection

This paper introduces a vision-based tactile sensor FingerVision, and explores its usefulness in tactile behaviors. FingerVision consists of a transparent elastic skin marked with dots, and a camera that is easy to fabricate, low cost, and physically robust. Unlike other vision-based tactile sensors, the complete transparency of the FingerVision skin provides multimodal sensation. The modalities sensed by FingerVision include distributions of force and slip, and object information such as distance, location, pose, size, shape, and texture. The slip detection is very sensitive since it is obtained by computer vision directly applied to the output from the FingerVision camera. It provides high-resolution slip detection, which does not depend on the contact force, i.e., it can sense slip of a lightweight object that generates negligible contact force. The tactile behaviors explored in this paper include manipulations that utilize this feature. For example, we demonstrate that grasp adaptation with FingerVision can grasp origami, and other deformable and fragile objects such as vegetables, fruits, and raw eggs.

scholarly journals Slip detection with accelerometer and tactile sensors in a robotic hand model

2015 ◽  
Vol 99 ◽  
pp. 012001 ◽  
Author(s):  
Abdulrahman Abdulkareem S Al-Shanoon ◽  
Siti Anom Ahmad ◽  
Mohd. Khair b. Hassan
Keyword(s):  
Robotic Hand ◽  
Tactile Sensors ◽  
Hand Model ◽  
Slip Detection
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