Sai Hong Tang
Papers
7
Total Citations
134
H-Index
5
About
Sai Hong Tang is a leading researcher in robotics, specializing in motion planning, obstacle avoidance, and autonomous navigation for mobile robots in dynamic and unknown environments. Their seminal review, "A review on motion planning and obstacle avoidance approaches in dynamic environments" (2015, 46 citations), synthesizes over 80 studies to provide a critical framework for navigating robots amidst moving obstacles—a foundational resource for the field. Tang’s work on automatic navigation in unknown environments (2013, 40 citations) advances real-time pathfinding without pre-mapped data, while their innovative use of artificial neural networks (ANN) to predict manipulator motion (2014, 26 citations) simplifies complex kinematic and trajectory calculations. They further demonstrate versatility by integrating ANN with forward kinematics for continuous trajectory solving (2014, 10 citations) and developing a genetic-based optimized fuzzy-tabu controller for randomized navigation (2013, 5 citations). More recently, Tang explores deep depth prediction and visual SLAM (2021, 4 citations), addressing challenges in accurate depth estimation for enhanced camera tracking. Their contributions also extend to humanoid robot walking pattern generation (2014, 3 citations), deriving simplified ZMP motion formulas from human gait analysis. With a cumulative impact spanning over 130 citations, Tang’s work is essential reading for researchers advancing intelligent, adaptive robotic systems.
Research Focus
Key Achievements
Top Papers
- 1
- 2Automatic navigation of mobile robots in unknown environments40 citations · 2013
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- 6Online Mutual Adaptation of Deep Depth Prediction and Visual SLAM4 citations · 2021
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