Papers
21
Total Citations
187
H-Index
8
About
Changman Son is a robotics and intelligent systems researcher whose career has been defined by a sustained focus on autonomous robot manipulation, part assembly, and intelligent motion planning. Over more than two decades, Son has developed and refined sophisticated control frameworks that blend fuzzy logic, neural networks, sensor fusion, and entropy-based learning to enable robotic systems to operate effectively in partially unknown or dynamic environments. His most influential contribution, an intelligent rule-based sequence planning algorithm with fuzzy optimization for robot manipulation tasks (2015, 31 citations), exemplifies his core approach: equipping robots with adaptive decision-making capabilities that can handle real-world uncertainty. Earlier foundational works, including his 2002 paper on optimal control planning with fuzzy entropy and sensor fusion (28 citations) and his 2001 neural/fuzzy process model (20 citations), established the theoretical scaffolding that underpins much of his subsequent research. Son has also made notable advances in path-finding methodologies using both fuzzy and crisp entropies, and has systematically compared intelligent planning algorithms across macro- and micro-assembly contexts. His work on stability analysis and learning-based motion strategies further demonstrates his commitment to rigorous, deployable solutions. With a body of work spanning foundational theory to practical algorithm design, Son's research has meaningfully shaped the field of intelligent robotic assembly.
Research Focus
Key Achievements
Top Papers
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- 3A neural/fuzzy optimal process model for robotic part assembly20 citations · 2001
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