Feng-Min Chang
Papers
2
Total Citations
6
H-Index
2
About
Feng-Min Chang’s research centers on autonomous mobile robotics, with a particular focus on perception, tracking, and human-robot interaction in dynamic indoor environments. His major contributions lie in developing robust algorithms for pedestrian tracking and target following, enabling robots to operate safely and effectively alongside humans. In his 2014 paper, “Inverse observation model and multiple hypothesis tracking for indoor mobile robots,” he introduced a modified inverse observation model to detect moving points in grid-based maps, achieving reliable target tracking in unknown environments. This work, cited 3 times, addresses a critical challenge in real-world robot deployment. His 2012 study, “Polar grid based robust pedestrian tracking with indoor mobile robot using multiple hypothesis tracking algorithm,” further advanced the field by employing a polar grid representation to improve tracking accuracy and directional awareness of moving targets. With 3 citations, this paper underscores his ability to translate theoretical tracking methods into practical robotic systems. Chang’s research is notable for its emphasis on robust, real-time performance, laying groundwork for service robots that can seamlessly integrate into human spaces. His work continues to inspire advancements in autonomous navigation and human-aware robotics.
Research Focus
Key Achievements
Top Papers
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- 2