Masaki Kadota
Papers
2
Total Citations
12
H-Index
2
About
Masaki Kadota is a researcher in swarm robotics and evolutionary computation, whose work focuses on enabling decentralized coordination in multi-robot systems. His most-cited paper, "Adaptive role assignment for self-organized flocking of a real robotic swarm" (2016, 7 citations), introduces a novel method for robots to dynamically allocate roles—such as leader or follower—without central control, achieving robust flocking behavior in physical robot swarms. This contribution addresses a fundamental challenge in swarm intelligence: how to maintain cohesion and adaptability in real-world environments. Kadota also advanced computational efficiency in evolutionary swarm robotics with his 2014 study on GPU-accelerated food-foraging simulations (5 citations), demonstrating how parallel processing can speed up the evolution of swarm controllers. His work bridges theory and practice, offering scalable solutions for autonomous systems. While his citation counts reflect a focused, early-career impact, his research is notable for its emphasis on real-robot validation—a critical step often overlooked in simulation-only studies. Kadota’s contributions are valuable for students and researchers interested in self-organizing systems, distributed robotics, and the intersection of hardware and algorithm design.
Research Focus
Key Achievements
Top Papers
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