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Yu Nojima

dblp:116/6405 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2012
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1Systems, architecture and hardware · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Robot manipulation · 100%

Topics — the 5 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Robotics › Robot manipulation › tactile sensing › vision-based tactile sensing
optical tactile sensing
0.112012
Basic experiments of three-axis tactile sensor using optical flow · ICRA 2012
Robotics › Robot manipulation
tactile sensing
0.112012
Basic experiments of three-axis tactile sensor using optical flow · ICRA 2012
Robotics › Robot manipulation › tactile sensing › tactile sensor design
three-axis tactile sensor
0.112012
Basic experiments of three-axis tactile sensor using optical flow · ICRA 2012
Robotics › Robot manipulation
force sensing
0.012012
Basic experiments of three-axis tactile sensor using optical flow · ICRA 2012
Robotics › Robot manipulation
grasping
0.012012
Basic experiments of three-axis tactile sensor using optical flow · ICRA 2012

Methods — techniques the papers use, named apart from their topics

optical flow · 0.1image processing · 0.1UV-LIGA · 0.1
YearPublicationVenuePosition
2012 Basic experiments of three-axis tactile sensor using optical flow
abstract
Three-axis tactile sensing has advantages for grasping an object of unknown mass and hardness. We developed a new three-axis tactile sensor that possesses a simple structure to endure a large applied force from a powerful grasp. Normal force distribution is measured based on grayscale values obtained by image data processing, as with previous three-axis tactile sensors. Tangential force distribution is determined by the linear movement of image data calculated by optical flow. The sensing characteristics of this sensor are dominated by the configuration and material of fine conical feelers formed on a silicon rubber sheet. By UV-LIGA, we obtain a fine mold of a silicon rubber sheet. In evaluation experiments, we applied both normal and tangential force to the sensor and confirmed this tactile sensor's ability to acquire normal and tangential forces. In its design, we utilize a USB microscope that has a CMOS camera and a light source. In a series of experiments, we performed normal and tangential force tests to obtain its basic characteristics. The linear relationship between the grayscale value and the normal force is obtained from the normal force test. If the average optical flow is under 0.2 mm, the tangential force is proportional to the average optical flow. The slope of the relationship between the tangential force and the average optical flow increases with additional normal force. Finally, we derive a series of equations for three-axis force calculation.
Masahiro Ohka, Takuya Matsunaga, Yu Nojima, Daiji Noda, Tadashi Hattori
ICRA3