Papers

9

Total Citations

67

H-Index

5

About

Bongsu Hahn is a robotics and control systems researcher whose work spans two distinct but complementary domains: low-power piezoelectric micro-robotics and autonomous mobile robot navigation. His early and most influential contributions focused on the challenging problem of energy-efficient actuation in microscale robotic systems, where he developed novel on-off switching control strategies for thin-film piezoelectric actuators operating under severe power constraints. His 2010 paper on optimal low-power control of piezoelectric rotational actuators remains his most cited work with 22 citations, and his broader body of work on model-free iterative adaptive controllers established practical frameworks for driving capacitive micro-actuators with limited sensing and significant model uncertainty — a persistent challenge in bio-inspired terrestrial micro-robot design. More recently, Hahn has turned his attention to autonomous mobile robotics, contributing enhanced local path planning methods that improve upon the Dynamic Window Approach for dynamic obstacle avoidance, as well as accessible sensing and control architectures for search-and-rescue robots. This latter work reflects a broader commitment to practical, low-cost robotic systems deployable in real-world emergency scenarios. Across his career, Hahn's research consistently prioritizes resource-constrained robotic systems, bridging microscale actuation physics with real-world autonomous navigation challenges.

Research Focus

Key Achievements

5
H-Index
9
Papers
67
Total Citations
7
Avg Citations/Paper
🏆 Most Cited Paper
Modeling and Optimal Low-Power On–Off Control of Thin-Film Piezoelectric Rotational Actuators
22 citations · 2010
📈 Most Prolific Year: 2011 (2 Papers)
🤝 Key Collaborators: 8
🏛 Institutions: University of Michigan–Ann Arbor, Hongik University, Kyungil University

Top Papers

  1. 1
  2. 2
  3. 3
  4. 4
  5. 5
  6. 6
  7. 7
  8. 8
  9. 9

Key Collaborators

Contact & Links

Available for collaboration
Content generated · 14 days ago