Multimodal Sensing by a Vision-Based Tactile Sensor Using a Deformable Touchpad

Author(s):  
Y. Ito ◽  
G. Obinata ◽  
Y. Kim ◽  
C. Nagai
2021 ◽  
Vol 11 (11) ◽  
pp. 5256
Author(s):  
Bo-Gyu Bok ◽  
Jin-Seok Jang ◽  
Min-Seok Kim

Modern robots fall behind humans in terms of the ability to discriminate between textures of objects. This is due to the fact that robots lack the ability to detect the various tactile modalities that are required to discriminate between textures of objects. Hence, our research team developed a robot fingertip module that can discriminate textures of objects via direct contact. This robot fingertip module is based on a tactile sensor with multimodal (3-axis force and temperature) sensing capabilities. The multimodal tactile sensor was able to detect forces in the vertical (Z-axis) direction as small as 0.5 gf and showed low hysteresis error and repeatability error of less than 3% and 2% in the vertical force measurement range of 0–100 gf, respectively. Furthermore, the sensor was able to detect forces in the horizontal (X- and Y-axes) direction as small as 20 mN and could detect 3-axis forces with an average cross-talk error of less than 3%. In addition, the sensor demonstrated its multimodal sensing capability by exhibiting a near-linear output over a temperature range of 23–35 °C. The module was mounted on a motorized stage and was able to discriminate 16 texture samples based on four tactile modalities (hardness, friction coefficient, roughness, and thermal conductivity).


2020 ◽  
Vol 2020 (17) ◽  
pp. 2-1-2-6
Author(s):  
Shih-Wei Sun ◽  
Ting-Chen Mou ◽  
Pao-Chi Chang

To improve the workout efficiency and to provide the body movement suggestions to users in a “smart gym” environment, we propose to use a depth camera for capturing a user’s body parts and mount multiple inertial sensors on the body parts of a user to generate deadlift behavior models generated by a recurrent neural network structure. The contribution of this paper is trifold: 1) The multimodal sensing signals obtained from multiple devices are fused for generating the deadlift behavior classifiers, 2) the recurrent neural network structure can analyze the information from the synchronized skeletal and inertial sensing data, and 3) a Vaplab dataset is generated for evaluating the deadlift behaviors recognizing capability in the proposed method.


2011 ◽  
Vol 25 (2) ◽  
pp. 129-134
Author(s):  
Guanghui Cao ◽  
Ying Huang ◽  
Wu Zhang ◽  
Caixia Liu

2013 ◽  
Vol 27 (1) ◽  
pp. 57-63
Author(s):  
Ying Huang ◽  
Wei Miao ◽  
Leiming Li ◽  
Wenting Cai ◽  
Qinghua Yang ◽  
...  
Keyword(s):  

Sensors ◽  
2019 ◽  
Vol 19 (4) ◽  
pp. 966 ◽  
Author(s):  
Marco Costanzo ◽  
Giuseppe De Maria ◽  
Ciro Natale ◽  
Salvatore Pirozzi

This paper presents the design and calibration of a new force/tactile sensor for robotic applications. The sensor is suitably designed to provide the robotic grasping device with a sensory system mimicking the human sense of touch, namely, a device sensitive to contact forces, object slip and object geometry. This type of perception information is of paramount importance not only in dexterous manipulation but even in simple grasping tasks, especially when objects are fragile, such that only a minimum amount of grasping force can be applied to hold the object without damaging it. Moreover, sensing only forces and not moments can be very limiting to securely grasp an object when it is grasped far from its center of gravity. Therefore, the perception of torsional moments is a key requirement of the designed sensor. Furthermore, the sensor is also the mechanical interface between the gripper and the manipulated object, therefore its design should consider also the requirements for a correct holding of the object. The most relevant of such requirements is the necessity to hold a torsional moment, therefore a soft distributed contact is necessary. The presence of a soft contact poses a number of challenges in the calibration of the sensor, and that is another contribution of this work. Experimental validation is provided in real grasping tasks with two sensors mounted on an industrial gripper.


IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 179028-179038
Author(s):  
Isibor Kennedy Ihianle ◽  
Augustine O. Nwajana ◽  
Solomon Henry Ebenuwa ◽  
Richard I. Otuka ◽  
Kayode Owa ◽  
...  

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