Utilization of Image/Force/Tactile Sensor Data for Object-shape-oriented Manipulation: Wiping Objects with Turning Back Motions and Occlusion

2021 ◽  
pp. 1-1
Author(s):  
Namiko Saito ◽  
Takumi Shimizu ◽  
Tetsuya Ogata ◽  
Shigeki Sugano
Keyword(s):  
Image Force ◽  
Tactile Sensor ◽  
Sensor Data ◽  
Object Shape

Thermal sensor for object shape and material constitution

Robotica ◽  
1988 ◽  
Vol 6 (1) ◽  
pp. 31-34 ◽  
Author(s):  
R. Andrew Russell
Keyword(s):  
Sensor Array ◽  
Robot Manipulator ◽  
Tactile Sensor ◽  
Sensor Data ◽  
Thermal Sensor ◽  
Material Constitution ◽  
Object Shape ◽  
Tactile Sensor Array

SUMMARYThis paper describes a novel tactile sensor array designed to provide information about the material constitution and shape of objects held by a robot manipulator. The sensor is modeled on the thermal touch sense which enables humans to distinguish between different materials based on how warm or cold they feel. Some results are presented and methods of analysing the sensor data are discussed.

scholarly journals Combining Self-Organizing and Graph Neural Networks for Modeling Deformable Objects in Robotic Manipulation

2020 ◽  
Vol 7 ◽  
Author(s):  
Angel J. Valencia ◽  
Pierre Payeur
Keyword(s):  
Robotic Manipulation ◽  
Sensor Data ◽  
Machine Learning Techniques ◽  
Robotic Hand ◽  
Deformable Objects ◽  
Object Shape ◽  
Learning Techniques ◽  
Data Driven Approach ◽  
Graph Neural Networks ◽  
Analytical Approaches

Modeling deformable objects is an important preliminary step for performing robotic manipulation tasks with more autonomy and dexterity. Currently, generalization capabilities in unstructured environments using analytical approaches are limited, mainly due to the lack of adaptation to changes in the object shape and properties. Therefore, this paper proposes the design and implementation of a data-driven approach, which combines machine learning techniques on graphs to estimate and predict the state and transition dynamics of deformable objects with initially undefined shape and material characteristics. The learned object model is trained using RGB-D sensor data and evaluated in terms of its ability to estimate the current state of the object shape, in addition to predicting future states with the goal to plan and support the manipulation actions of a robotic hand.

VisuoTactile 6D Pose Estimation of an In-Hand Object using Vision and Tactile Sensor Data

2022 ◽  
pp. 1-1
Author(s):  
Snehal Dikhale ◽  
Karankumar Patel ◽  
Daksh Dhingra ◽  
Itoshi Naramura ◽  
Akinobu Hayashi ◽  
...  
Keyword(s):  
Pose Estimation ◽  
Tactile Sensor ◽  
Sensor Data

Development of a tactile sensor based on optical fiber specklegram analysis and sensor data fusion technique

2017 ◽  
Vol 263 ◽  
pp. 677-686 ◽  
Author(s):  
Eric Fujiwara ◽  
Yu Tzu Wu ◽  
Murilo Ferreira Marques dos Santos ◽  
Egont Alexandre Schenkel ◽  
Carlos Kenichi Suzuki
Keyword(s):  
Data Fusion ◽  
Optical Fiber ◽  
Tactile Sensor ◽  
Sensor Data ◽  
Fusion Technique ◽  
Sensor Data Fusion

Analysis of High Pressure Zone Attributes From Tactile Pressure Sensor Field Data

2014 ◽  
Author(s):  
Martin Richard ◽  
Rocky S. Taylor
Keyword(s):  
High Pressure ◽  
Indentation Test ◽  
Field Measurements ◽  
Tactile Sensor ◽  
Threshold Value ◽  
Companion Paper ◽  
Sensor Data ◽  
Pressure Zone ◽  
Pressure Threshold ◽  
High Pressure Zone

Tactile sensor data collected during the Japan Ocean Industries Association (JOIA) medium-scale field indentation test program provide detailed information about spatial and temporal distributions of contact pressures during ice crushing. The localization of contact into high pressure zones (hpzs) through which the majority of loads are transmitted to the structure is an important feature of these data. For all but the slowest interaction rates, non-simultaneous failure is observed, with linear distributions of hpzs comprising a total contact area on the order of 10% of the nominal interaction area (structure width × ice thickness). To improve understanding of the nature of individual hpzs during compressive ice failure, a new approach to analyzing tactile sensor data has been developed. Analysis algorithms developed for automatic hpz detection and tracking are discussed. Issues associated with pressure threshold value definition and selection are considered. Probabilistic descriptions of high pressure zone attributes based on analysis of JOIA field measurements are presented. The development of a probabilistic ice load model based on these hpz data is detailed in a companion paper.

OBJECT-SHAPE RECOGNITION BY TACTILE IMAGE ANALYSIS USING SUPPORT VECTOR MACHINE

2014 ◽  
Vol 28 (04) ◽  
pp. 1450011 ◽  
Author(s):  
ANWESHA KHASNOBISH ◽  
ARINDAM JATI ◽  
GARIMA SINGH ◽  
AMIT KONAR ◽  
D. N. TIBAREWALA
Keyword(s):  
Support Vector Machine ◽  
Image Analysis ◽  
Shape Recognition ◽  
Tactile Sensor ◽  
Computational Time ◽  
Support Vector ◽  
Object Shape ◽  
Tactile Image ◽  
Misclassification Rates ◽  
Object Shapes

The sense of touch is important to human to understand shape, texture, and hardness of the objects. An object under grip, i.e. object exploration by enclosure, provides a unique pressure distribution on the different regions of palm depending on its shape. This paper utilizes the above experience for recognition of object shapes by tactile image analysis. The high pressure regions (HPRs) are segmented and analyzed for object shape recognition rather than analyzing the entire image. Tactile images are acquired by capacitive tactile sensor while grasping a particular object. Geometrical features are extracted from the chain codes obtained by polygon approximation of the contours of the segmented HPRs. Two-level classification scheme using linear support vector machine (LSVM) is employed to classify the input feature vector in respective object shape classes with an average classification accuracy of 93.46% and computational time of 1.19 s for 12 different object shape classes. Our proposed two-level LSVM reduces the misclassification rates, thus efficiently recognizes various object shapes from the tactile images.

Object-shape recognition and 3D reconstruction from tactile sensor images

2014 ◽  
Vol 52 (4) ◽  
pp. 353-362 ◽  
Author(s):  
Anwesha Khasnobish ◽  
Garima Singh ◽  
Arindam Jati ◽  
Amit Konar ◽  
D. N. Tibarewala
Keyword(s):  
3D Reconstruction ◽  
Shape Recognition ◽  
Tactile Sensor ◽  
Object Shape
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