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About

K. Urakawa is a robotics researcher whose work centers on sensor-based path planning and motion planning for mobile robots navigating uncertain, unknown environments. His major contributions focus on developing near-optimal algorithms that minimize search costs—such as the number of visited points or total moving distance—while ensuring robust navigation around obstacles. Notably, his 2003 paper “Three or more dimensional sensor-based path-planning algorithm HD-I” (17 citations) addresses the limitations of optimal algorithms like A* in online settings, proposing a more efficient approach for higher-dimensional spaces. His 2002 work on parts mating (6 citations) introduces a near-optimal algorithm for deadlock-free paths in environments with a single unknown obstacle, demonstrating practical applications in automated assembly. Urakawa also refined classic algorithms like Class1 and Bug2 for 2D environments cluttered with simple shapes, achieving near-optimal performance with minimal sensor data. Though his citation counts are modest, his research provides foundational insights into balancing computational efficiency with navigational reliability, making his work valuable for students and engineers designing real-time robotic systems in unpredictable settings.