Toshiharu Sugawara
Papers
15
Total Citations
87
H-Index
6
About
Toshiharu Sugawara is a researcher whose work sits at the intersection of multi-agent systems, autonomous robotics, and cooperative task coordination. His research focuses predominantly on developing intelligent algorithms that enable groups of autonomous agents and robots to collaborate efficiently on large-scale, continuous tasks — particularly in cleaning, patrolling, and area coverage domains. Sugawara's most significant contributions lie in designing decentralized learning and task allocation strategies that allow agents to coordinate without relying on centralized control or constant communication. His 2013 work on decentralized area partitioning (12 citations) and autonomous learning for coordinated cleaning tasks (11 citations) established foundational approaches to how robots can independently learn and adapt their behavior in shared environments. His 2016 paper on divisional cooperation in multi-agent patrolling (11 citations) further advanced efficient autonomous task allocation methodologies. Beyond cleaning and patrolling, Sugawara has tackled combinatorial optimization challenges in multi-agent systems, notably applying double-layered ant colony optimization to the coalition structure generation problem. His earlier work also extends to mobile robot position control, demonstrating a breadth spanning both theoretical and applied robotics. Collectively, his publications reflect a sustained commitment to making autonomous multi-robot cooperation practical, adaptive, and scalable for real-world deployment.
Research Focus
Key Achievements
Top Papers
- 1Decentralized Area Partitioning for a Cooperative Cleaning Task12 citations · 2013
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