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About

Andrew Chen is a researcher specializing in robotics, computer vision, and autonomous systems software architecture. His work bridges the gap between theoretical frameworks and real-world robotic applications, with a particular focus on enabling robots to perceive and interact meaningfully with their environments. Chen's most recognized contributions center on robotic chess-playing systems, where he developed sophisticated computer vision algorithms enabling humanoid robots — most notably the Baxter platform — to autonomously perceive real-world game states and compete against human players. His 2019 paper on robust chess analysis and interaction has garnered 24 citations, matching his equally influential 2017 work on dynamic and collaborative automation software systems. The latter addresses the critical challenge of heterogeneous hardware integration in manufacturing environments, proposing middleware-based approaches to streamline robotic system design. Beyond vision systems, Chen has contributed to indoor autonomous navigation, presenting a single RF emitter-based method applicable to factory automation and search-and-rescue scenarios. His framework work using the SystemJ language further demonstrates his commitment to building interoperable, scalable robotics software. Collectively, his publications have accumulated over 75 citations, establishing him as a meaningful contributor to applied robotics and intelligent systems research.