Papers
36
Total Citations
1,082
H-Index
15
About
Yugang Liu is a robotics researcher whose work spans modular robot design, mobile manipulation, and urban search and rescue (USAR) systems. His most influential contribution is a comprehensive survey on robotic USAR from a control perspective (2013, 268 citations), which has become a foundational reference in the field. Building on this, Liu pioneered semi-autonomous and learning-based control frameworks that intelligently balance human operator input with robot autonomy during disaster response missions — work that attracted over 100 citations each and continues to shape human-robot teaming research. Earlier in his career, Liu made significant advances in modular and reconfigurable robotics, developing spring-assisted manipulator designs, fuzzy and neural-fuzzy controllers for tip-over prevention, and parameter identification techniques for modular systems. His tracked mobile robot research addressed the critical challenge of stair-climbing stability, producing practical online tipover prediction algorithms applicable to real rescue platforms. Across more than a decade of research, Liu's contributions bridge theoretical control design and real-world deployment of rescue robots. With a body of work exceeding 800 cumulative citations, his research has meaningfully advanced autonomous navigation, cooperative multirobot coordination, and safe human-robot collaboration in hazardous environments.
Research Focus
Key Achievements
Top Papers
- 1Robotic Urban Search and Rescue: A Survey from the Control Perspective268 citations · 2013
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- 9Parameter Identification and Vibration Control in Modular Manipulators30 citations · 2004
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