Kunihiko Nakazono
Papers
2
Total Citations
13
H-Index
2
About
Kunihiko Nakazono is a robotics researcher whose work focuses on intelligent control systems for autonomous and manipulator robots, blending machine learning, fuzzy logic, and evolutionary algorithms. His most cited paper, "Force and position control of robot manipulator using neurocontroller with GA based training" (2004, 7 citations), addresses the classic challenge of simultaneous force and position control in robotic arms. By proposing a neurocontroller trained with a genetic algorithm, Nakazono demonstrated how neural networks can learn complex hybrid control tasks without explicit mathematical modeling—a contribution that has informed adaptive robotics design. His more recent work, "Fuzzy controller for AUV robots based on machine learning and genetic algorithm" (2023, 6 citations), extends this approach to autonomous underwater vehicles, integrating fuzzy logic with GA-optimized learning for robust navigation in uncertain environments. Though his citation counts are modest, Nakazono’s research bridges classical control theory with modern machine learning, offering practical pathways for developing adaptive, self-tuning robots. His work is particularly valuable for students and engineers exploring neuro-fuzzy systems and evolutionary optimization in real-world robotic applications, from industrial manipulators to underwater exploration.
Research Focus
Key Achievements
Top Papers
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