Ming Han

Hebei University of Technology

Papers

8

Total Citations

49

H-Index

4

About

Ming Han is a robotics and mechanical engineering researcher whose work spans parallel mechanism design, robot kinematics, trajectory optimization, and construction robotics. His research addresses fundamental challenges in robotic systems, particularly the limitations of conventional parallel mechanisms — including restricted workspaces, singular configurations, and insufficient load capacity. His most-cited work (2024, 12 citations) introduces a fully redundant-drive planar 6R parallel mechanism with optimized dimensional design, representing a meaningful advance in high-performance robotic architecture. Complementing this, his investigations into hybrid force-position control and error compensation strategies (2024) demonstrate a commitment to improving both precision and reliability in real-world robotic applications. Han's work extends prominently into construction robotics, where he has developed specialized systems for external cladding installation, addressing safety and quality concerns in demanding industrial environments. His application of multi-objective evolutionary algorithms — particularly NSGA-II — to trajectory planning for articulated heavy-duty and dual-arm robots reflects a sophisticated approach to balancing efficiency, energy consumption, and mechanical wear. With a growing body of work accumulating over 49 citations across eight publications, Han is establishing himself as a focused contributor to the intersection of mechanism theory, applied robotics, and intelligent construction automation.

Research Focus

Key Achievements

4
H-Index
8
Papers
49
Total Citations
6
Avg Citations/Paper
🏆 Most Cited Paper
Performance evaluation and dimensional optimization design of planar 6R redundant actuation parallel mechanism
12 citations · 2024
📈 Most Prolific Year: 2024 (4 Papers)
🤝 Key Collaborators: 18
🏛 Institutions: Hebei University of Technology

Top Papers

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Key Collaborators

Contact & Links

Available for collaboration
Content generated · 13 days ago