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About

Jen-Shiun Chiang is a researcher specializing in autonomous robotics, computer vision, and humanoid robot systems, with a particular focus on the competitive domain of RoboCup soccer. His work has made meaningful contributions to solving two of the most challenging problems in autonomous robotics: real-time object recognition and accurate self-localization in dynamic, unpredictable environments. Chiang's most cited work, "A Stereo Vision-Based Self-Localization System" (2013, 30 citations), exemplifies his signature approach of integrating stereo vision with pan/tilt motor data to enable humanoid robots to precisely determine their position on the field — a capability critical for effective game strategy and navigation. This research evolved through multiple iterations, including earlier monocular vision approaches and RoboCup-specific implementations, demonstrating his sustained commitment to refining these methods. Beyond localization, Chiang has developed neural network-based self-localization techniques and adaptive resolution methods for object recognition, addressing real-time processing constraints that are essential for competitive robotic applications. His collective body of work, accumulating over 60 citations, has advanced the practical deployment of vision systems in humanoid robots. Students interested in robotics perception, machine vision, or autonomous systems will find Chiang's research a valuable reference for bridging theoretical computer vision with real-world robotic competition challenges.