Shaobin Lan

Tianjin University

Papers

2

Total Citations

61

H-Index

2

About

Shaobin Lan is a researcher specializing in compliant actuation and mechanical design, with a focus on developing adaptive, safe robotic systems for unstructured environments and rehabilitation applications. His major contributions center on advancing variable stiffness compliant actuators, which offer superior impedance control and bandwidth compared to constant-stiffness counterparts. Lan’s most-cited work, “A new mechanical design method of compliant actuators with non-linear stiffness with predefined deflection-torque profiles” (2018, 44 citations), introduces a novel approach for tailoring actuator stiffness to specific torque-deflection requirements, enabling more precise and versatile robotic interactions. His earlier study, “Design of a New Nonlinear Stiffness Compliant Actuator and Its Error Compensation Method” (2016, 17 citations), further refines this technology by addressing accuracy limitations through error compensation techniques. These contributions address critical challenges in human-robot interaction, where safety and adaptability are paramount. Lan’s work has been instrumental in bridging the gap between theoretical compliance models and practical actuator implementations, earning recognition among robotics and mechanical engineering communities. His research continues to influence the design of next-generation compliant systems for rehabilitation robots and human-assistive devices, with cumulative citations reflecting growing interest in his innovative design methodologies.

Research Focus

Key Achievements

2
H-Index
2
Papers
61
Total Citations
31
Avg Citations/Paper
🏆 Most Cited Paper
A new mechanical design method of compliant actuators with non-linear stiffness with predefined deflection-torque profiles
44 citations · 2018
📈 Most Prolific Year: 2018 (1 Papers)
🤝 Key Collaborators: 2
🏛 Institutions: Tianjin University

Top Papers

  1. 1
  2. 2

Key Collaborators

Contact & Links

Available for collaboration
Content generated · 12 days ago