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Sha Ye

dblp:135/8722 · DBLP profile ↗
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1ranked-venue papers
1as first author
0since 2021 · last 2013
—ORCID · unresolved

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

Artificial intelligence and machine learning · 1 · 1 first-authorSystems, architecture and hardware · 1 · 1 first-author

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 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Robotics › Robot manipulation
grasping
0.212013
Robust robotic grasping using IR Net-Structure Proximity Sensor to handle objects with unknown position and attitude · ICRA 2013
Robotics › Robot manipulation › grasping › grasp stability
grasp robustness
0.212013
Robust robotic grasping using IR Net-Structure Proximity Sensor to handle objects with unknown position and attitude · ICRA 2013

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

proximity sensing · 0.2pre-shaping · 0.2object positioning · 0.2
YearPublicationVenuePosition
2013 Robust robotic grasping using IR Net-Structure Proximity Sensor to handle objects with unknown position and attitude
abstract
In this paper, we focus on unknown parameters such as the position and attitude of the object, and describe a short-range, high-speed and noncontact sensing method for obtaining the position and attitude of the object using IR Net-Structure Proximity Sensor (“IR-NSPS”) which complements the dead region of sensory information between visual and tactile sensing. To be more precise, we propose two effective control methods which are pre-shaping and object positioning using IR-NSPS for robust grasping by adjusting the gripper configuration in response to attitude error of up to ±45 deg and the position error of up to ±80 mm of the unknown object. The methods therefore can significantly increase the speed and effectiveness of grasping objects without requiring a specific approach that depends on a vision sensor. Furthermore, to demonstrate the advantages of pre-shaping and object positioning, object grasping experiments were performed using these two operations to grasp objects placed randomly on a tabletop.
Sha Ye, Yosuke Suzuki, Masatoshi Ishikawa, Makoto Shimojo
ICRA1