Donghwi Shin

Papers

Total Citations

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About

Donghwi Shin is an emerging researcher specializing in autonomous robotics, intelligent fleet management, and smart manufacturing systems. His work sits at the intersection of artificial intelligence, digital twin technology, and factory automation, with a particular focus on revolutionizing how large-scale robot fleets operate in industrial environments. Shin's most notable contribution is the development of the Autonomous Robot Orchestration Solution (AROS), a framework designed to transform the management of Overhead Hoist Transport (OHT) systems in semiconductor and advanced manufacturing facilities. By integrating machine learning and digital twin technologies, AROS enables individual robots to identify their own states and environmental conditions, collaborating intelligently toward shared operational goals. His 2024 paper introducing this system has already garnered 3 citations, with a follow-up 2025 study advancing the approach further using Active Q Routing algorithms to optimize fleet-level decision-making. Though early in his research career, Shin is addressing a critical challenge in modern factory automation — efficiently coordinating massive robot fleets at scale. His interdisciplinary approach, combining reinforcement learning, simulation-based digital twins, and real-world robotics applications, positions him as a promising contributor to the future of intelligent manufacturing systems.