Seongin Na
Papers
8
Total Citations
185
H-Index
6
About
Seongin Na is a leading researcher in swarm robotics, specializing in bio-inspired communication and autonomous navigation systems. His work centers on developing artificial pheromone frameworks and deep reinforcement learning (DRL) controllers that enable large-scale robot swarms to coordinate without centralized control. Na’s most impactful contribution is his bio-inspired collision avoidance system for autonomous vehicle swarms, which uses DRL to achieve safe, decentralized navigation—a paper that has garnered 62 citations and is foundational for future transportation safety. He also pioneered a federated reinforcement learning approach for collective swarm navigation (47 citations), allowing individual robots to learn shared policies while preserving data privacy. His artificial pheromone system (46 citations), inspired by social insect communication, provides a scalable, low-bandwidth method for swarm coordination in dynamic environments. Na’s extended pheromone model adds diffusion and advection features, enhancing flexibility for real-world applications. With over 185 total citations, his work bridges biological principles and machine learning, offering practical solutions for deploying myriad robot swarms in search-and-rescue, environmental monitoring, and autonomous transport.
Research Focus
Key Achievements
Top Papers
- 1
- 2Federated Reinforcement Learning for Collective Navigation of Robotic Swarms47 citations · 2023
- 3Bio-inspired artificial pheromone system for swarm robotics applications46 citations · 2020
- 4Extended Artificial Pheromone System for Swarm Robotic Applications10 citations · 2019
- 5Reinforcement learning-based aggregation for robot swarms8 citations · 2023
- 6
- 7Toward a Myriad Robot Swarm Aggregation4 citations · 2022
- 8