Papers
2
Total Citations
42
H-Index
2
About
Chia-Ming Chang is a pioneering researcher in robotics and autonomous systems, with a career spanning over three decades. His foundational work in collision-free motion planning for articulated robot arms, published in 1990, introduced a novel approach using minimum distance functions and geometric modeling—representing robot links as cylinders capped with hemispheres. This paper, which has garnered 37 citations, remains a cornerstone in robotic path planning, enabling safer and more efficient multi-arm operations in shared 3D workspaces. More recently, Chang has advanced into deep learning applications for autonomous systems. His 2022 study on YOLO-based deep learning for needle-type dashboard recognition represents a significant leap toward fully automatic auxiliary flying systems. By implementing a modified YOLO object detection model to read analog airspeed indicators, his work bridges classical robotics with modern computer vision, achieving 5 citations in a rapidly evolving field. This research demonstrates his enduring ability to adapt cutting-edge AI techniques to practical autonomous maneuvering challenges. Chang’s career exemplifies a rare trajectory—from foundational geometric algorithms to contemporary deep learning—making him a versatile figure whose work continues to inspire both roboticists and AI researchers.
Research Focus
Key Achievements
Top Papers
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