Papers
6
Total Citations
256
H-Index
5
About
Yunhua Wu is a leading researcher in space robotics and autonomous control systems, with a primary focus on on-orbit servicing technologies. Her work addresses critical challenges in space operations, particularly in the areas of dual-arm trajectory planning, attitude control, and dynamics modeling for free-floating and multi-armed space robots. Wu’s most influential contribution is her 2020 paper on reinforcement learning for dual-arm trajectory planning, which has garnered 182 citations, demonstrating its significant impact on the field. She has also made notable advances in hybrid actuator control for on-orbit servicing spacecraft (31 citations) and dynamics modeling for multi-armed robots (22 citations). Her research extends to adaptive estimation techniques for malfunctioned satellites, a key enabler for robotic servicing missions, and probabilistic movement primitives for multi-task learning. Wu’s work is characterized by its integration of deep reinforcement learning with traditional path planning methods, as seen in her 2024 paper on improved RRT path planning. Her contributions are essential for advancing autonomous space operations, making her a pivotal figure in the development of next-generation space servicing systems.
Research Focus
Key Achievements
Top Papers
- 1
- 2Attitude control for on-orbit servicing spacecraft using hybrid actuator31 citations · 2018
- 3
- 4Probabilistic movement primitives based multi-task learning framework10 citations · 2024
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