Papers
11
Total Citations
231
H-Index
7
About
Dae Sun Hong is a robotics and automation researcher whose career spans several decades of foundational work in robotic assembly planning and humanoid robot motion optimization. He is best known for pioneering the application of computational intelligence techniques—including neural networks, genetic algorithms, and simulated annealing—to the complex problem of generating optimal robotic assembly sequences. His 1995 neural-network-based approach to assembly sequence generation remains his most influential contribution, accumulating 68 citations, while his subsequent genetic algorithm and simulated annealing studies further established him as a leading voice in this niche. A distinctive thread in Hong's research is his rigorous treatment of assembly constraints, cost minimization, and disassemblability analysis, the latter offering a mathematical framework for deriving stable assembly sequences by quantifying the difficulty of part removal. In the mid-2000s, Hong expanded his focus toward humanoid robotics, investigating cooperative motion control and posture optimization using genetic algorithms and dynamic modeling of multi-body systems. His later work on transfer robot component selection reflects a continued commitment to practical industrial applications. Across more than two decades, Hong's research has provided engineers and roboticists with versatile, algorithmically grounded tools for automating and optimizing complex assembly tasks.
Research Focus
Key Achievements
Top Papers
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- 3Generation of robotic assembly sequences using a simulated annealing29 citations · 2003
- 4Disassemblability analysis for generating robotic assembly sequences25 citations · 2002
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- 6Optimization of robotic assembly sequences using neural network17 citations · 2002
- 7Posture optimization for a humanoid robot using a simple genetic algorithm12 citations · 2010
- 8Self-learning control of cooperative motion for a humanoid robot6 citations · 2006
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