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About

Hsu-Yang Chang is a leading researcher in autonomous robotics, with a primary focus on simultaneous localization and mapping (SLAM), multi-robot coordination, and high-precision local positioning systems. His most influential contribution is the development of **P-SLAM**, a prediction-based SLAM algorithm that innovatively forecasts environmental structure in unexplored regions—a significant departure from traditional SLAM methods that only map already-visited areas. This work has garnered 77 citations and remains a foundational reference in the field. Chang also advanced multi-robot SLAM (MR-SLAM) by addressing critical challenges such as map fusion and unknown robot poses, earning 64 citations for his collaborative exploration framework. Beyond theoretical contributions, he demonstrated real-world impact by engineering a high-accuracy local positioning system for an autonomous robotic golf greens mower, achieving centimeter-level precision through active beacons and lateration. His work on global posture estimation using RFID and laser scanning further showcases his ability to integrate diverse sensor modalities for robust robot navigation. With over 170 total citations across his key publications, Chang’s research bridges the gap between algorithmic innovation and practical deployment, making him a notable figure in intelligent robotics.