Jonguk Kim

Papers

Total Citations

194

H-Index

About

Jonguk Kim is a leading researcher in autonomous navigation and multi-robot systems, with a core focus on integrating reinforcement learning and computer vision for intelligent robotics. His most impactful work, the 2019 paper "Multi-Robot Path Planning Method Using Reinforcement Learning" (189 citations), introduces a groundbreaking algorithm that combines Deep Q-learning with Convolutional Neural Networks (CNNs). This approach revolutionizes conventional path planning by eliminating the need for robots to search wide areas or move in rigid formations, instead enabling them to learn efficient, adaptive navigation strategies through experience. The work has become a cornerstone for researchers tackling complex multi-agent coordination problems. Kim also contributes to the critical field of sensor calibration, as demonstrated in his 2021 study on stereo camera distortion and rectification (5 citations). This work addresses the essential challenge of providing accurate, reliable visual input for advanced vehicle systems, including smart cars and mobile robots that must detect obstacles and markings in real-world environments. Through his dual focus on learning-based planning and robust perception, Kim is advancing the practical deployment of autonomous systems in dynamic, unstructured settings.