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Xiangjin Zeng

dblp:28/1723 · DBLP profile ↗
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2ranked-venue papers
0as first author
1since 2021 · last 2024
0000-0003-1931-0167ORCID · reported

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

Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 since 2021Applied, interdisciplinary, general and emerging computing · 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
Motion planning and robot control · 100%

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

TopicWeightPapersLastEvidence papers
Robotics › Motion planning and robot control › robot control
adaptive control
0.112011
Improved Stability Results for Visual Tracking of Robotic Manipulators Based on the Depth-Independent Interaction Matrix · IEEE Trans. Robotics 2011
Robotics › Motion planning and robot control
robot control
0.112011
Improved Stability Results for Visual Tracking of Robotic Manipulators Based on the Depth-Independent Interaction Matrix · IEEE Trans. Robotics 2011
Robotics › Motion planning and robot control › robot control › sensor-based control
visual tracking control
0.112011
Improved Stability Results for Visual Tracking of Robotic Manipulators Based on the Depth-Independent Interaction Matrix · IEEE Trans. Robotics 2011

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

lyapunov stability · 0.1depth-independent interaction matrix · 0.1
YearPublicationVenuePosition
2024 Face super resolution based on attention upsampling and gradient
Anyi Zheng, Xiangjin Zeng, Pengpeng Song, Yong Mi, Zhibo He
Multim. Tools Appl.2
2011 Improved Stability Results for Visual Tracking of Robotic Manipulators Based on the Depth-Independent Interaction Matrix
abstract
As we know, the dynamic visual-tracking control, which is based on the depth-independent interaction matrix, has been proposed to cope with the general 3-D motion of robot manipulators. To deal with the unknown camera parameters, an adaptive law has been designed. It is noted, however, that the designed adaptive law uses a vector signal defined by the true depths of feature points, which are unavailable when the camera parameters are unknown. Nevertheless, such an adaptive law can still be implemented by replacing the exact depths with the estimated depths, since the estimated depths can be readily calculated by using the estimated values of the camera parameters. In this case, however, we cannot definitely say whether or not the robot system can be guaranteed to achieve asymptotical stability or even stable behavior. To overcome this problem, in this paper, we redefine the mentioned vector signal using the estimated depths and show that the design based on the new vector signal can make the robot system asymptotically stable. Additionally, the existing tracking control design based on the concept of depth-independent interaction matrix is initial-state dependent. Thus, in this paper, we also modify the existing design to obtain an initial-state-independent result. To show the performance of the proposed designs, simulation results based on a two-link planar manipulator are presented. In addition, preliminary experimental results using an industrial manipulator are also given.
Xinwu Liang, Xinhan Huang, Min Wang 0002, Xiangjin Zeng
IEEE Trans. Robotics4