Xuexiang Huang

Papers

5

Total Citations

42

H-Index

3

About

Dr. Xuexiang Huang is a leading researcher in space robotics, specializing in the kinematics, path planning, and control of free-floating and hyper-redundant manipulators for on-orbit operations. His work addresses the fundamental challenge of controlling space robots while conserving linear and angular momentum—a constraint that induces complex kinematic coupling between the satellite base and its arm. Dr. Huang’s major contributions include pioneering the use of deep reinforcement learning for path planning, as demonstrated in his highly cited work on MRDDPG algorithms (14 citations), which overcomes the difficulties of multiple constraints and poor adaptability in free-floating manipulation. He also developed a weighted augmented Jacobian matrix method (17 citations) to enhance human–robot motion similarity in teleoperation, and a minimal dataset construction method for capture position recognition (6 citations). Most recently, Dr. Huang has advanced the field with a novel hybrid active-passive cable-driven hyper-redundant manipulator and its force-position-model fusion control, designed for operations in narrow, constrained environments. His research is critical for enabling safe, precise, and autonomous space robotics, with direct applications in satellite servicing, debris removal, and in-space assembly.

Research Focus

Key Achievements

3
H-Index
5
Papers
42
Total Citations
8
Avg Citations/Paper
🏆 Most Cited Paper
Weighted augmented Jacobian matrix with a variable coefficient method for kinematics mapping of space teleoperation based on human–robot motion similarity
17 citations · 2016
📈 Most Prolific Year: 2018 (3 Papers)
🤝 Key Collaborators: 14

Top Papers

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Key Collaborators

Contact & Links

Available for collaboration
Content generated · 14 days ago