Ynog-Tae Kim

Papers

Total Citations

H-Index

About

Ynog-Tae Kim is a researcher specializing in logistics robotics, autonomous navigation, and multi-agent systems for industrial automation. His most cited work, "A Study on Improved Navigation Algorithm of Logistics Transportation Robot for Freight Moving on the Rack" (2018, 3 citations), addresses critical challenges in warehouse automation. Kim proposes a weighted priority-based collision avoidance algorithm combined with SLAM (Simultaneous Localization and Mapping) to enhance the navigation of multiple logistics transport robots. His key contribution lies in improving upon the standard D* Lite algorithm, which treats other robots as dynamic obstacles, forcing inefficient path recalculations. Instead, Kim’s approach enables coordinated, cooperative navigation, allowing robots to adjust paths more intelligently and reduce delays—vital for high-throughput logistics centers. Though his citation count is modest, the work demonstrates practical impact by targeting real-world efficiency bottlenecks in freight-moving robots. Kim’s research bridges theoretical path-planning with applied robotics, offering solutions that could streamline warehouse operations and reduce energy consumption. His focus on multi-robot coordination and SLAM-based localization positions him as a contributor to the growing field of autonomous material handling, with potential applications in e-commerce and smart manufacturing.