Papers
15
Total Citations
251
H-Index
7
About
Jing Xin is a leading researcher in intelligent robotics, specializing in deep reinforcement learning for autonomous navigation, multi-robot cooperative systems, and visual servoing. Her pioneering work on applying deep reinforcement learning to mobile robot path planning—enabling robots to derive optimal actions directly from raw visual perception without hand-crafted features—has garnered 118 citations and established a foundational approach in the field. She further advanced autonomous navigation by developing systems that allow robots to reach desired positions using only visual observations, eliminating the need for pre-built maps. In multi-robot coordination, Xin introduced a cooperative localization system using ultrawideband sensors and GPU acceleration to overcome non-line-of-sight errors in complex indoor environments, achieving 26 citations. Her contributions extend to intelligent logistics, where she designed a fast multi-robot navigation system addressing e-commerce demands, and to visual servoing, where she tackled field-of-view constraints with adaptive zooming strategies. With over 230 total citations across her top works, Jing Xin’s research continues to shape the future of autonomous robotics, offering practical solutions for real-world navigation and manipulation challenges.
Research Focus
Key Achievements
Top Papers
- 1Application of deep reinforcement learning in mobile robot path planning118 citations · 2017
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- 4Multi-Mobile Robot Autonomous Navigation System for Intelligent Logistics19 citations · 2018
- 5Visual navigation for mobile robot with Kinect camera in dynamic environment11 citations · 2016
- 6Visual servoing of robot manipulator with weak field-of-view constraints10 citations · 2021
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- 8Depth Adaptive Zooming Visual Servoing for a Robot with a Zooming Camera6 citations · 2013
- 9Robot visual sliding mode servoing using SIFT features5 citations · 2016
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