Papers
9
Total Citations
66
H-Index
4
About
Masao Kubo is a pioneering researcher in swarm robotics and collective decision-making, with a focus on developing decentralized algorithms that enable robot swarms to operate autonomously and robustly. His key research areas include energy distribution in distributed autonomous systems, multi-target enclosure, and consensus algorithms for best-of-n problems. Kubo's major contributions include the concept of "trophallaxis" for maintaining energy balance in robot swarms, inspired by biological systems, and novel enclosure models that allow swarms to surround multiple targets without requiring individual recognition, enhancing scalability and fault tolerance. His work on agreement algorithms using trial-and-error methods at the macrolevel addresses the best-of-proportions problem, offering a robust approach to collective decision-making. With over 60 citations across his most-cited papers, Kubo's research has influenced practical applications such as disaster site surveillance and herding tasks. Notable achievements include his 2008 paper on collective energy distribution and his 2014 work on multiple targets enclosure, both of which are foundational in swarm robotics. His recent exploration of sheepdog-inspired herding agents further demonstrates his commitment to bio-inspired solutions for complex swarm behaviors.
Research Focus
Key Achievements
Top Papers
- 1
- 2Multiple targets enclosure by robotic swarm18 citations · 2014
- 3Agreement algorithm using the trial and error method at the macrolevel9 citations · 2018
- 4
- 5Herd guidance by multiple sheepdog agents with repulsive force4 citations · 2022
- 6Target Enclosure for Multiple Targets4 citations · 2012
- 7
- 8Swarm based enclosure model for unspecified number of targets2 citations · 2011
- 9Individual recognition-free target enclosure model2 citations · 2012