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Dejun Guo

Researcher at University of Utah

Publications -  17
Citations -  480

Dejun Guo is an academic researcher from University of Utah. The author has contributed to research in topics: Robot & Adaptive control. The author has an hindex of 7, co-authored 17 publications receiving 280 citations. Previous affiliations of Dejun Guo include Shanghai Jiao Tong University.

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Journal ArticleDOI

Robots Under COVID-19 Pandemic: A Comprehensive Survey

TL;DR: A survey comprehensively reviews over 200 reports covering robotic systems which have emerged or have been repurposed during the past several months, to provide insights to both academia and industry as mentioned in this paper.
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Adaptive Vision-Based Leader–Follower Formation Control of Mobile Robots

TL;DR: The proposed adaptive controller only requires the image information from an uncalibrated perspective camera mounted at any position and orientation (attitude) on the follower robot and does not depend on the relative position measurement and communication between the leader and follower.
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Adaptive Image-Based Trajectory Tracking Control of Wheeled Mobile Robots With an Uncalibrated Fixed Camera

TL;DR: A depth-independent image Jacobian matrix framework for the wheeled mobile robots will be developed such that unknown parameters in the closed-loop system can be linearly parameterized and adaptive laws can be designed to estimate the unknown parameters online and the depth information of the feature point can be allowed to be time varying.
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Eye-in-Hand Tracking Control of a Free-Floating Space Manipulator

TL;DR: This paper studies the visual-tracking (visual-servo) control problem for a free-floating space manipulator using an eye-in-hand camera for the capturing process, where the motion of the target spacecraft is unknown and the dynamics of the system are uncertain.
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Spatial-Temporal Trajectory Redesign for Dual-Stage Nanopositioning Systems With Application in AFM

TL;DR: A new systematic range-and-temporal-based trajectory-redesign process is presented, where the desired trajectory is first split based on achievable positioning bandwidth, and then, split spatially based on the achievable range and positioning resolution.