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Kentaro Nozu

dblp:181/3901 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2016
—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 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Robotics › Robot manipulation › grasping
adaptive grasping
0.212016
Robotic grasp control with high-resolution combined tactile and proximity sensing · ICRA 2016
Robotics › Robot manipulation › tactile sensing › vision-based tactile sensing
optical tactile sensing
0.112016
Robotic grasp control with high-resolution combined tactile and proximity sensing · ICRA 2016

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

stereo matching · 0.2infrared imaging · 0.2compound-eye camera · 0.2
YearPublicationVenuePosition
2016 Robotic grasp control with high-resolution combined tactile and proximity sensing
abstract
We have proposed a multimodal sensing with optical device and its application to robotic grasp control. The proposed device provides both tactile and proximity information with high spatial resolution on the same scale of coordinates, that is necessary while grasping small and complicated-shaped objects using a robotic hand. In our proposed device, tactile information is obtained as infrared image through a light conductive plate, which allows the capture of a tactile image of high spatial resolution using a camera. Proximity information is detected based on stereo matching of a pair of images obtained by two cameras. By using a compound-eye camera, we constructed a compact device so that the sensor can be mounted on the robotic hand. Through experiments with 6 DoF robotic arm and robotic hand equipped with the proposed sensing device, we show that the robotic motion for searching, approaching and grasping can be controlled based on sensor information obtained from only the proposed device. In addition, our combined sensing approach enables an adaptive grasping motion of the robotic hand.
Kazuhiro Shimonomura, Hiroto Nakashima, Kentaro Nozu
ICRA3