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Jing-Chen Peng

dblp:359/2364 · DBLP profile ↗
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1ranked-venue papers
1as 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 first-author · 1 since 2021Systems, architecture and hardware · 1 · 1 first-author · 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 · 67% Motion planning and robot control · 33%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Robotics › Motion planning and robot control › robot dynamics › contact dynamics
contact force analysis
0.812024
3D Force and Contact Estimation for a Soft-Bubble Visuotactile Sensor Using FEM · ICRA 2024
Robotics › Robot manipulation › force sensing
force estimation
0.812024
3D Force and Contact Estimation for a Soft-Bubble Visuotactile Sensor Using FEM · ICRA 2024
Robotics › Robot manipulation
tactile sensing
0.812024
3D Force and Contact Estimation for a Soft-Bubble Visuotactile Sensor Using FEM · ICRA 2024

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

linear plane stress approximation · 0.8finite element method · 0.8RGB-D sensing · 0.8
YearPublicationVenuePosition
2024 3D Force and Contact Estimation for a Soft-Bubble Visuotactile Sensor Using FEM
abstract
Soft-bubble tactile sensors have the potential to capture dense contact and force information across a large contact surface. However, it is difficult to extract contact forces directly from observing the bubble surface because local contacts change the global surface shape significantly due to membrane mechanics and air pressure. This paper presents a model-based method of reconstructing dense contact forces from the bubble sensor’s internal RGBD camera and air pressure sensor. We present a finite element model of the force response of the bubble sensor that uses a linear plane stress approximation that only requires calibrating 3 variables. Our method is shown to reconstruct normal and shear forces significantly more accurately than the state-of-the-art, with comparable accuracy for detecting the contact patch, and with very little calibration data.
Jing-Chen Peng, Shaoxiong Yao, Kris Hauser
ICRA1