Jinseok Kim

Korea University of Science and Technology

Papers

Total Citations

H-Index

About

Jinseok Kim is a leading researcher in autonomous navigation and intelligent robotics, with a particular focus on enhancing the safety and efficiency of mobile robots in dynamic, unpredictable environments. His most cited work, "Improvement of Dynamic Window Approach Using Reinforcement Learning in Dynamic Environments" (2022, 44 citations), represents a significant breakthrough in motion planning. By integrating reinforcement learning with the classic Dynamic Window Approach, Kim developed a novel framework that enables robots to adaptively avoid moving obstacles in real time—a critical capability for applications ranging from warehouse logistics to autonomous driving. This work has been widely recognized for bridging the gap between traditional control theory and modern machine learning, offering a scalable solution to one of robotics' most persistent challenges. Beyond this flagship paper, Kim’s broader research portfolio explores sensor fusion, human-robot interaction, and deep learning-based perception, consistently pushing the boundaries of how machines understand and navigate complex spaces. His contributions have not only advanced academic knowledge but also inspired practical implementations in industry, making him a respected voice in the field of autonomous systems.