Ryosuke Kamoi

National Institute of Technology, Toyama College

Papers

Total Citations

H-Index

About

Ryosuke Kamoi is a robotics researcher whose work focuses on motion planning and autonomous manipulation for articulated robotic systems. His key research areas include path planning for multi-degree-of-freedom manipulators, obstacle avoidance algorithms, and efficient search-based motion strategies. Kamoi’s major contribution lies in developing a fast motion planning method for 6 DOF robot arms using a maze-searching approach. He introduced the Closest Point First Search (CPFS) algorithm, a novel adaptation inspired by Lumelsky’s Bug2 algorithm, enhanced with a Footprint Avoidance Principle (FAP) to improve computational efficiency and collision avoidance in complex environments. This work, published in 2010, has garnered 4 citations and laid groundwork for more responsive robotic navigation in cluttered spaces. Kamoi’s research is particularly valuable for industrial automation and service robotics, where real-time, safe motion planning is critical. His approach demonstrates a clever fusion of classical bug algorithms with modern search techniques, offering a practical solution for high-dimensional configuration spaces. For students and researchers in robotics, Kamoi’s work exemplifies how foundational algorithms can be creatively adapted to solve real-world manipulation challenges.