Papers
2
Total Citations
11
H-Index
2
About
Jun-Han Oh is a researcher in robotics and automation, with a primary focus on multi-robot coordination and intelligent task planning. His work addresses fundamental challenges in enabling multiple robots to operate efficiently and safely in shared environments. Oh’s most notable contribution is a centralized decoupled path planning algorithm that uses temporary goal configurations to resolve inter-robot collisions, a method that ensures feasible, collision-free paths for robots with individual start and goal configurations. This work, published in 2011, has garnered 6 citations and remains relevant for applications in warehouse logistics and manufacturing. In parallel, Oh advanced the field of service robotics by applying optimal supervisory control theory to task planning. His 2010 paper introduced a cost-efficient framework that uses discrete event systems to assign tasks to multiple service robots without degrading service quality, achieving 5 citations. By integrating cost functions into supervisory control, Oh demonstrated how to balance operational efficiency with performance, a key insight for real-world deployment. His research bridges theoretical control methods with practical robotics, offering scalable solutions for multi-agent systems.
Research Focus
Key Achievements
Top Papers
- 1
- 2Task planning for service robots with optimal supervisory control5 citations · 2010