Papers
3
Total Citations
80
H-Index
3
About
Jae-Hoon Kim is a leading researcher in mobile robotics, specializing in path planning and intelligent control for autonomous systems. His work addresses the fundamental challenge of enabling robots to navigate complex, dynamic environments safely and efficiently. Kim’s major contributions lie in developing novel optimization and learning-based algorithms. He pioneered a bi-population particle swarm optimization with a random perturbation strategy for static path planning, significantly improving solution quality and convergence speed over conventional methods. For dynamic settings, he introduced the Adaptive Soft Actor–Critic (ASAC) algorithm, a deep reinforcement learning framework that balances obstacle avoidance, trajectory smoothness, and path length in real time. His highly cited work—with papers accumulating 50 and 27 citations respectively—demonstrates the immediate impact and relevance of his approaches within the robotics community. Kim’s research also extends to industrial applications, including kinematic calibration for welding robots using the Indoor Global Positioning System (IGPS), showcasing his ability to bridge theoretical advances with practical, industry-driven solutions.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3