Masanori Goka
Papers
1
Total Citations
7
H-Index
1
About
Masanori Goka is a pioneering researcher in swarm robotics and evolutionary computation, whose work focuses on designing decentralized control systems for homogeneous autonomous robot collectives. His most-cited paper, "Cooperative Transport by a Swarm Robotic System Based on CMA-NeuroES Approach" (2013, 7 citations), addresses a fundamental challenge in swarm intelligence: how to generate emergent, system-level behaviors without global controllers. Goka introduced a novel neuro-evolutionary approach combining Covariance Matrix Adaptation (CMA) with Neuro-Evolution of Augmenting Topologies (NeuroES), enabling robots to dynamically coordinate complex tasks like cooperative transport through local interactions. This work demonstrates how evolutionary algorithms can optimize neural network controllers for swarm systems, achieving robust performance in unpredictable environments. Goka’s contributions are particularly significant for advancing autonomous multi-robot systems in logistics, search-and-rescue, and environmental monitoring, where scalability and adaptability are critical. By bridging evolutionary computation and swarm robotics, he provides a framework for designing resilient, self-organizing robot teams that operate without centralized oversight—a key step toward practical, real-world swarm deployments.
Research Focus
Key Achievements
Top Papers
- 1