Tianjian Hu
Papers
8
Total Citations
80
H-Index
6
About
Tianjian Hu is a pioneering researcher in space robotics and human-robot interaction, whose work bridges the gap between autonomous manipulation and artistic expression. His primary research areas include kinematics mapping for teleoperation, deep reinforcement learning for path planning, and disturbance rejection control for space systems. Hu’s most influential contribution is the development of a weighted augmented Jacobian matrix with a variable coefficient method, which significantly improves human-robot motion similarity in space teleoperation—a paper that has garnered 17 citations. He also introduced the MRDDPG algorithm (14 citations), a deep reinforcement learning approach that overcomes the challenges of multi-constraint path planning for free-floating space robots, enhancing adaptability in complex orbital environments. Beyond technical robotics, Hu has explored robotic art, designing a musical robot capable of performing the Chinese bamboo flute (13 citations), showcasing the intersection of engineering and culture. His work on obstacle avoidance for redundant manipulators using a backward quadratic search algorithm (10 citations) and active disturbance rejection control for teleoperation (7 citations) further underscores his impact. With over 80 total citations across his key papers, Hu’s research is essential reading for students and engineers advancing autonomous space systems and human-robot collaboration.
Research Focus
Key Achievements
Top Papers
- 1
- 2MRDDPG Algorithms for Path Planning of Free-Floating Space Robot14 citations · 2018
- 3Designing a musical robot for Chinese bamboo flute performance13 citations · 2019
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- 6Active disturbance rejection controller for space teleoperation7 citations · 2012
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