Papers
1
Total Citations
9
H-Index
1
About
Mengying Tang is a pioneering researcher in space robotics, specializing in intelligent motion planning and control for multi-arm robotic systems. Her work bridges deep reinforcement learning and autonomous manipulation, addressing critical challenges in on-orbit servicing and assembly. Her most cited paper, "Coordinated Motion Planning of Dual-arm Space Robot with Deep Reinforcement Learning" (2019, 9 citations), introduces a novel framework that combines Denavit-Hartenberg kinematic modeling with rapidly-exploring random trees (RRT) and reinforcement learning to enable real-time, collision-free coordination between dual robotic arms in microgravity environments. This contribution is foundational for advancing autonomous space operations, reducing reliance on human teleoperation. Tang’s research has significant implications for future space missions, including satellite repair and debris removal. Her work is recognized for its innovative integration of learning-based methods with classical robotics, offering scalable solutions for complex, dynamic space tasks. With growing citation impact, Tang is establishing herself as a key figure in intelligent space robotics, inspiring new directions in autonomous systems for extreme environments.
Research Focus
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Top Papers
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