Kazushi Nakano
Papers
12
Total Citations
52
H-Index
4
About
Kazushi Nakano is a robotics and autonomous systems researcher whose work spans multi-robot coordination, path planning, collision avoidance, and reinforcement learning. His research addresses some of the most practically challenging problems in robotics: how groups of autonomous robots can navigate complex environments safely, coordinate their behavior intelligently, and learn effective control strategies with minimal human supervision. Nakano's most notable contributions include developing online trajectory modification techniques that enable multiple robots to avoid collisions while adhering to reference paths — a problem with direct implications for industrial automation and autonomous vehicle systems. His work on leader-following formation navigation and deadlock-free path control further demonstrates a sustained commitment to making multi-robot systems robust in real-world conditions. He has also explored game-theoretic frameworks for multi-agent coordination, applying equilibrium-switching strategies to target tracking tasks, and investigated reinforcement learning reward allocation methods applied to challenging stabilization problems such as the triple inverted pendulum. Beyond coordination and control, Nakano has contributed to applied robotics through StRRT-based path planning with PSO-tuned parameters for RoboCup soccer and sliding mode control for flexible link manipulators. Though his citation counts are modest, his body of work reflects a consistently rigorous and broad engagement with autonomous systems research, offering valuable foundations for students entering multi-robot and intelligent control fields.
Research Focus
Key Achievements
Top Papers
- 1StRRT-based path planning with PSO-tuned parameters for RoboCup soccer7 citations · 2014
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