Xibin Cao
Papers
7
Total Citations
49
H-Index
4
About
Xibin Cao is a leading researcher at the forefront of space robotics and intelligent control systems, with a focus on making autonomous robots more robust and adaptable in dynamic, real-world environments. His work bridges critical gaps between high-level task planning and low-level motion control, particularly for space applications. Cao’s most influential contribution is a novel fault diagnosis scheme for control moment gyroscopes, leveraging an attention-enhanced convolutional neural network (21 citations), which significantly improves the reliability of spacecraft attitude control. He has also pioneered a priority-based switching model predictive control method for sequential space robot manipulation (6 citations), enabling efficient execution of complex, multi-step tasks. Further demonstrating his impact, Cao has advanced the feasibility of angles-only navigation for uncooperative satellite rendezvous (4 citations) and developed an air-bearing-based testbed for validating multi-arm space robot algorithms (4 citations). His recent work on modular multi-level replanning for Task and Motion Planning (TAMP) (11 citations) addresses the critical challenge of real-time plan adaptation under interference, pushing robots toward greater autonomy. Through these contributions—totaling over 49 citations—Cao is shaping the future of resilient, intelligent space robotics.
Research Focus
Key Achievements
Top Papers
- 1
- 2Modular Multi-Level Replanning TAMP Framework for Dynamic Environment11 citations · 2024
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- 5Design and Demonstration for an Air-bearing-based Space Robot Testbed4 citations · 2022
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