Welcome

Thank you for visiting my profile page.

I am a M.S. candidate in Information and Communication Engineering at Myongji University (MJU), advised by Prof. Jaehee Jung .

My master's research focused on enhancing dynamic obstacle avoidance performance in mobile robot navigation by leveraging deep learning models to process semantic information and generate human-like trajectories based on imitation learning.

My primary research interests are focused on:

Learning-based path planning

Imitation learning

Vision–Language–Action model

Robot Development and Implementation

All of these projects were conducted solely by myself.

Swerve-Drive Robot

Omni-Wheel-Based Robot

Differential-Drive Robot

Research

(* denotes equal contribution)

1. HADP: Hybrid A*-Diffusion Planner for Robust Navigation in Dynamic Obstacle Environments

Mingyu Kim*, Chanyeong Heo*, Jaehee Jung

IEEE Access (2026.9) Impact Factor: 3.6

We proposed the Hybrid A*-Diffusion Planner (HADP) to overcome the generalization and data-efficiency limitations of pure diffusion-based planning in dynamic environments. Our approach uses A* for global path planning in obstacle-free regions and a conditional diffusion model fed by a semantic map, robot pose, and local goal to generate responsive local avoidance trajectories upon detecting dynamic obstacles.

2. HiMSELF: A Hierarchical Misbehavior Classification with Sequence Embedding by Latent Features in Vehicular Ad-Hoc Networks [Link]

Mingyu Kim, Dae Hyun Yum, Jaehee Jung

IEEE Access (2026.8) Impact Factor: 3.6

We propose HiMSELF, a misbehavior classification system that learns latent sequence embeddings of multi-class BSM data, applies hierarchical clustering to construct a two-stage classification hierarchy, and achieves an average F1-score of 0.9918 on 19 misbehavior classes, outperforming existing models.