Papers
6
Total Citations
51
H-Index
4
About
Wenbing Tang is a leading researcher in multi-robot systems and autonomous navigation, with a focus on robust motion planning and collision avoidance. Their work addresses critical challenges in real-world robotics, particularly the vulnerability of deep reinforcement learning (DRL)-based planners to cyber-attacks. Tang pioneered the integration of fuzzy rules with velocity obstacles for cooperative collision avoidance, achieving a balance between computational efficiency and safety guarantees. They have also developed causal deconfounding DRL techniques to improve generalization in motion planning, and introduced GAN-based frameworks to defend against localization and position deception attacks. With papers accumulating over 50 citations, including "Cooperative Collision Avoidance in Multirobot Systems Using Fuzzy Rules and Velocity Obstacles" (13 citations) and "Causal Deconfounding Deep Reinforcement Learning for Mobile Robot Motion Planning" (12 citations), Tang’s contributions are shaping the next generation of secure, efficient, and resilient autonomous systems. Their work is essential reading for researchers seeking to bridge the gap between theoretical robustness and practical deployment in contested environments.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3
- 4
- 5Dynamic Algorithm Selection for Mobile Robots Motion Planning3 citations · 2020
- 6