Shinichi Takeyama

National Institute of Technology, Toyama College

Papers

Total Citations

H-Index

About

Shinichi Takeyama is a robotics researcher whose work centers on motion planning and autonomous manipulation for articulated robotic systems. His primary contributions lie in developing efficient algorithms for high-degree-of-freedom (DOF) manipulators, particularly focusing on real-time pathfinding in complex environments. His most cited work, "Motion Planning for 6 DOF Robot Arm Based on Maze-searching" (2010), introduces a novel approach that adapts the Closest Point First Search (CPFS) algorithm—inspired by Lumelsky’s Bug2 method—to solve motion planning for six-axis arms. By integrating a Footprint Avoidance Protocol (FAP), Takeyama’s method enables faster, collision-free trajectories without exhaustive computation, achieving practical gains for industrial and service robotics. Though his citation count (4) reflects a niche but specialized audience, his work bridges classical bug algorithms with modern manipulator control, offering a computationally light alternative to sampling-based planners. Takeyama’s research is particularly valuable for students and engineers seeking intuitive, deterministic solutions to motion planning in constrained workspaces, demonstrating how bio-inspired search strategies can be effectively repurposed for robotic arm navigation.