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Shengwen Xie

dblp:224/8575 · DBLP profile ↗
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1ranked-venue papers
1as first author
1since 2021 · last 2021
—ORCID · unresolved

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
Motion planning and robot control · 70% Video understanding and tracking · 23% Robot manipulation · 7%

Topics — the 5 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Robotics › Motion planning and robot control › robot control
force control
0.512021
A Hybrid Position/Force Controller for Joint Robots · ICRA 2021
Robotics › Motion planning and robot control › robot control › compliant motion control
hybrid position/force control
0.512021
A Hybrid Position/Force Controller for Joint Robots · ICRA 2021
Computer vision › Video understanding and tracking
motion tracking
0.512021
A Hybrid Position/Force Controller for Joint Robots · ICRA 2021
Robotics › Motion planning and robot control
robot control
0.512021
A Hybrid Position/Force Controller for Joint Robots · ICRA 2021
Robotics › Robot manipulation
dual-arm manipulation
0.112021
A Hybrid Position/Force Controller for Joint Robots · ICRA 2021

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

trajectory manipulation · 0.5joint space to task space mapping · 0.5
YearPublicationVenuePosition
2021 A Hybrid Position/Force Controller for Joint Robots
abstract
In this paper, we present a hybrid position/force controller for operating joint robots. The hybrid controller has two goals—motion tracking and force regulating. As long as these two goals are not mutually exclusive, they can be decoupled in some way. In this work, we make use of the smooth and invertible mapping from the joint space to the task space to decouple the two control goals and design controllers separately. The traditional motion controller in the task space is used for motion control, while the force controller is designed through manipulating the desired trajectory to regulate the force indirectly. Two case studies—contour tracking/polishing surfaces and grabbing boxes with two robotic arms—are presented to show the efficacy of the hybrid controller, and simulations with physics engines are carried out to validate the efficacy of the proposed method.
Shengwen Xie, Juan Ren
ICRA1