Seyoung Oh

About

Seyoung Oh is a pioneer in intelligent robotics, specializing in neural network-based navigation, sensor fusion, and autonomous systems for mobile robots. His most influential work, "Evolving a modular neural network-based behavioral fusion using extended VFF and environment classification for mobile robot navigation" (23 citations), introduced a groundbreaking hybrid approach that combines rule-based potential fields with adaptive neural networks. This work, which extended the conventional Virtual Force Field (VFF) method, enabled robots to dynamically switch behaviors based on environmental classification—a foundational contribution to robust local navigation. Oh further advanced practical robotics with his 2011 study on "Integrated On-Line Localization, Mapping and Coverage Algorithm of Unknown Environments for Robotic Vacuum Cleaners Based on Minimal Sensing" (14 citations), which achieved complete coverage using low-cost sensors—a key innovation for consumer robotics. His earlier work on real-time visual servo tracking using Position Sensitive Detectors (PSDs) demonstrated how neural networks could learn complex sensor-motor associations for industrial robot control. Oh’s research bridges theoretical neural computation with real-world robotic applications, from vacuum cleaners to industrial manipulators, making him a notable figure in the evolution of intelligent, sensor-driven autonomous navigation.

Research Focus

Key Achievements

Top Papers

1. 1
   Evolving a modular neural network-based behavioral fusion using extended VFF and environment classification for mobile robot navigation

   23 citations · 2002
2. 2
   Integrated On-Line Localization, Mapping and Coverage Algorithm of Unknown Environments for Robotic Vacuum Cleaners Based on Minimal Sensing

   14 citations · 2011
3. 3
   A neural network based real-time robot tracking controller using position sensitive detectors

   4 citations · 2002

Key Collaborators

Contact & Links