Papers
4
Total Citations
19
H-Index
3
About
Yoshio Yokose is a robotics researcher whose work focuses on energy-efficient trajectory planning for industrial manipulators, addressing the critical need to reduce energy consumption in manufacturing. His major contributions lie in developing novel genetic algorithm (GA) approaches that optimize robot motion paths to minimize energy use while accounting for real-world complexities like nonlinear Coulomb friction. Yokose’s most-cited paper, “Energy-saving trajectory planning for robots using the genetic algorithm with assistant chromosomes” (2019, 8 citations), introduces a method that enhances GA performance by incorporating assistant chromosomes to solve trajectory optimization problems more effectively. His earlier work, “Trajectory planning for a manipulator with nonlinear Coulomb friction using a dynamically incremental genetic algorithm” (2016, 5 citations), tackles the challenge of friction-induced energy losses. Yokose’s research is driven by the urgent need to combat global warming and environmental destruction caused by mass energy consumption in factories. Though his citation counts are modest, his focused contributions to sustainable robotics are notable for their practical implications in reducing industrial energy footprints, making his work a valuable resource for researchers in green manufacturing and optimization algorithms.
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