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Pin-Chun Yeh

dblp:383/4465 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2024
—ORCID · none

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

Artificial intelligence and machine learning · 1 · 1 since 2021Systems, architecture and hardware · 1 · 1 since 2021

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
gripper design
0.812024
A Force-Controlled Gripper Capable of Measuring Mechanical Properties of an Object · ICRA 2024
Robotics › Robot manipulation › tactile sensing
object property estimation
0.212024
A Force-Controlled Gripper Capable of Measuring Mechanical Properties of an Object · ICRA 2024

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

optical encoder · 0.8drivetrain deformation sensing · 0.8
YearPublicationVenuePosition
2024 A Force-Controlled Gripper Capable of Measuring Mechanical Properties of an Object
abstract
Various sensorized grippers have been developed to handle delicate objects safely. These grippers have sensors mounted on their fingers’ surface that provide direct force measurements. However, multiple sensors are often required on one finger, leading to significant sensor placement and wire routing complexity. Finger-based sensors are limited to sensing external gripping force, and fingers cannot be easily replaced to meet the requirements of objects with specific geometries. To overcome the complexity and limitations of finger surface sensors, this paper proposes a force-controlled two-fingered gripper that relies on the deformation sensing of elastic elements in the drivetrain to obtain finger force. By using a minimum number of optical encoders placed in the drivetrain, accurate position and force sensing can be achieved at any location of each finger. When gripping an object, the size and stiffness of the object can thus be accurately measured. Simulation and experimental results demonstrate the proposed gripper’s merits. We expect this new gripper to provide a more competitive solution for robots that need to manipulate objects and check their mechanical qualities at the same time.
Yi-Shian Tsai, Pin-Chun Yeh, Chun-Hung Huang, I-Cheng Hsueh, Chao-Chieh Lan
ICRA2