Sangjin Park
Papers
1
Total Citations
4
H-Index
1
About
Dr. Sangjin Park is a rising scholar in the field of decentralized multi-robot systems and autonomous navigation, with a focus on overcoming fundamental limitations in reactive control algorithms. His most-cited work, "Escaping Local Minima: Hybrid Artificial Potential Field with Wall-Follower for Decentralized Multi-Robot Navigation" (2025), addresses a critical challenge in multi-robot coordination: the tendency of traditional Artificial Potential Field (APF) methods to become trapped in local minima when navigating environments with nonconvex obstacles and incomplete environmental knowledge. Park’s key contribution is a novel hybrid approach that integrates APF with a wall-following behavior, enabling robots to escape local minima and continue toward their goals without requiring global maps or centralized control. This work has already garnered 4 citations, signaling its early impact on the robotics community. By enhancing the robustness and scalability of decentralized navigation, Park’s research promises to advance applications in search-and-rescue, warehouse automation, and swarm robotics. His innovative synthesis of simplicity and reliability marks him as a promising researcher to watch in autonomous systems.
Research Focus
Key Achievements
Top Papers
- 1