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Xu Pei

dblp:246/7868 · DBLP profile ↗
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2ranked-venue papers
0as first author
1since 2021 · last 2025
—ORCID · unresolved

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

Artificial intelligence and machine learning · 2 · 1 since 2021Systems, 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 · 70% Motion planning and robot control · 30%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Robotics › Robot manipulation › robot design
robot arm design
0.412019
A Novel Rotating Beam Link for Variable Stiffness Robotic Arms · ICRA 2019
Robotics › Motion planning and robot control
robot control
0.412019
A Novel Rotating Beam Link for Variable Stiffness Robotic Arms · ICRA 2019
Robotics › Robot manipulation › robot design › manipulator design
variable stiffness link
0.412019
A Novel Rotating Beam Link for Variable Stiffness Robotic Arms · ICRA 2019
Robotics › Robot manipulation › contact modeling
mechanics modeling
0.112019
A Novel Rotating Beam Link for Variable Stiffness Robotic Arms · ICRA 2019

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

column buckling analysis · 0.4beam deflection modeling · 0.4
YearPublicationVenuePosition
2025 CCINet: A cascaded consensus interaction network for co-saliency object detection
Longsheng Wei, Xu Pei, Jiu Huang
Neurocomputing2
2019 A Novel Rotating Beam Link for Variable Stiffness Robotic Arms
abstract
In this paper, we present a novel design concept for a robot arm link with variable stiffness. Variable stiffness links are intended to grant a robot the safety benefits of compliance and the performance benefits of stiffness. Our compact design actively modulates stiffness via parallel, rotating beams actuated by simple servomotors. It achieves a lateral stiffness ratio greater than ten with a minimum stiffness under 0.2 N/mm. Our novel design offers many benefits over existing variable stiffness link solutions in its compactness, simplicity, and speed of actuation. One challenge of this research lies in the mechanics modeling of variable stiffness. Here we propose a comprehensive mechanics model that considers mechanical compliances due to deflections of parallel guided beams, column buckling, and bearing at the beam roots. By comparing with experimental testing data, we show that our analytical model accurately predicts the lateral stiffness of the robotic link. This model can be used as a design tool in future iterations, including for scaling the design.
Tyler N. Morrison, Xu Pei
ICRA3