Tomoya Kawabe
Papers
7
Total Citations
41
H-Index
4
About
Tomoya Kawabe is a robotics and optimization researcher whose work sits at the intersection of motion planning, multi-robot systems, and intelligent manufacturing. His research focuses on developing sophisticated algorithms that enable robots to operate more efficiently, safely, and autonomously across industrial and commercial environments. Kawabe's most significant contributions lie in simultaneous optimization of robot motion planning and spatial layout design. His pioneering use of sequence-pair and sequence-triple representations to solve complex packing and manufacturing layout problems has established a distinctive methodological signature across his work. He has advanced surrogate-assisted multi-objective evolutionary algorithms to tackle computationally demanding problems that would otherwise be intractable in real-time settings. Beyond manufacturing, Kawabe has made notable strides in multi-robot coordination, combining reinforcement learning approaches like Q-learning with classical graph search and sampling-based methods such as RRT* to achieve flexible, collision-free trajectory and route planning. His more recent explorations integrate large language models into robot task planning, reflecting a forward-looking interest in human-robot accessibility. With a growing body of work accumulating over 40 citations, Kawabe's research demonstrates both practical engineering relevance and theoretical innovation, making him a rising contributor to the fields of intelligent robotics and combinatorial optimization.
Research Focus
Key Achievements
Top Papers
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