Papers
10
Total Citations
197
H-Index
6
About
Bingheng Wang is a robotics and control systems researcher whose work spans aerial robotics, legged locomotion, and space robotics. His most influential contribution lies in motion planning and control for wheeled-bipedal robots, particularly his hierarchical framework for underactuated jumping motions, which has garnered over 95 citations and demonstrated how hybrid wheel-leg designs can be exploited for dynamic maneuvers. In aerial robotics, Wang has advanced robust quadrotor flight through the development of Neural Moving Horizon Estimation (NeuroMHE), a learning-based disturbance estimator that eliminates the need for extensive ground-truth training data, accumulating nearly 30 citations since 2023. He has further explored deep reinforcement learning for agile SE(3) motion planning in cluttered environments. Earlier in his career, Wang made notable contributions to tethered space robotics, designing anti-sway control, reel-based tension regulation, and orbital transfer strategies for debris removal missions — work that collectively reflects a strong foundation in model predictive control and estimation theory. Across these diverse domains, Wang consistently bridges classical control rigor with modern machine learning techniques, making his research highly relevant to researchers working at the frontier of autonomous and space robotic systems.
Research Focus
Key Achievements
Top Papers
- 1
- 2Neural Moving Horizon Estimation for Robust Flight Control29 citations · 2023
- 3
- 4Reel-Based Tension Control of Tethered Space Robots13 citations · 2019
- 5MPC-based anti-sway control of tethered space robots11 citations · 2018
- 6
- 7
- 8A towing orbit transfer method of tethered space robots5 citations · 2015
- 9
- 10Trust-Region Neural Moving Horizon Estimation for Robots3 citations · 2024