Andrew Jaeyong Choi

Korea Advanced Institute of Science and Technology, Gachon University

Papers

Total Citations

H-Index

About

Andrew Jaeyong Choi is a robotics researcher whose work focuses on autonomous systems, unmanned aerial vehicles (UAVs), and intelligent path planning. His most cited paper, "Automated Aerial Docking System Using Onboard Vision-Based Deep Learning" (2022, 24 citations), introduces a novel framework that integrates a docking mechanical system with a vision-based deep learning detection and tracking pipeline. This work addresses a fundamental challenge in mid-air UAV operations, enabling reliable autonomous docking without external infrastructure. In his more recent contribution, "Optimized Frontier-Based Path Planning Using the TAD Algorithm for Efficient Autonomous Exploration" (2024), Choi proposes a path-planning method that leverages trapezoid, adjacent, and distance (TAD) characteristics of frontiers. By using the mobile robot’s sensor range to detect and modify frontiers in real time, the algorithm significantly improves exploration efficiency. Though early in his career, Choi’s work demonstrates a clear trajectory toward solving core autonomy problems—combining perception, control, and planning. His research has immediate applications in search-and-rescue, infrastructure inspection, and autonomous navigation in GPS-denied environments.