Fumihiko Yano
Papers
2
Total Citations
18
H-Index
2
About
Fumihiko Yano is a robotics researcher whose work has focused on the challenging domain of multi-joint robot arm control and motion optimization. His research addresses one of the fundamental difficulties in robotic systems: the kinematic redundancy problem, where the position of a robotic end-effector cannot be uniquely determined by individual joint angles alone, making precise and efficient movement inherently complex to compute. Yano's most significant contributions center on applying evolutionary computation techniques — particularly genetic algorithms — to solve these redundancy challenges. His 2004 paper, "Optimizing Movement of A Multi-Joint Robot Arm with Existence of Obstacles Using Multi-Purpose Genetic Algorithm," represents a notable advancement by extending the problem to obstacle-avoidance scenarios, earning 12 citations in the field. This built upon his earlier 1999 work exploring preferable movement strategies for multi-joint arms using genetic algorithms, which garnered 6 citations. By framing robot arm motion planning as a multi-objective optimization problem and leveraging genetic algorithms as a solution mechanism, Yano contributed practical computational approaches to robotic path planning. His work is particularly relevant for researchers and students exploring bio-inspired optimization methods applied to real-world robotics and automation challenges.
Research Focus
Key Achievements
Top Papers
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