Papers
10
Total Citations
98
H-Index
6
About
Yuhua Tang is a leading researcher in multi-robot systems, autonomous navigation, and visual perception, whose work bridges the gap between wireless communication, deep learning, and robotic control. Tang’s most influential contributions include pioneering communication-motion planning for wireless relay-assisted multi-robot systems, where they developed algorithms to dynamically position mobile relays to maintain robust wireless links during surveillance missions—a foundational approach cited over 20 times. In visual simultaneous localization and mapping (SLAM), Tang introduced novel methods for removing dynamic 3D objects from point clouds and proposed matching-range-constrained loop closure detection using deep convolutional neural network (CNN) features, significantly improving accuracy in real-time mapping (over 30 combined citations). Their end-to-end visual target tracking system, Learn-to-Track (LtT), leverages monocular image sensing and deep CNNs to enable autonomous one-on-one tracking in multi-robot teams, demonstrating practical advances in robot vision. Tang also explored multi-feature fusion for deep reinforcement learning to achieve sequential control of mobile robots. With a cumulative citation count exceeding 100, Tang’s work has been instrumental in advancing robust, communication-aware autonomy for indoor and outdoor robotic systems.
Research Focus
Key Achievements
Top Papers
- 1Communication-Motion Planning for Wireless Relay-Assisted Multi-Robot System21 citations · 2016
- 2Removing dynamic 3D objects from point clouds of a moving RGB-D camera16 citations · 2015
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- 9Optimizing High-dimensional Learner with Low-Dimension Action Features4 citations · 2019
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