Hiroyuki Okuda
Papers
10
Total Citations
77
H-Index
6
About
Hiroyuki Okuda is a robotics researcher whose work spans robot vision, autonomous mobile robot navigation, and human-robot interaction. His early contributions focused on real-time vision systems, introducing a novel three-level broad-edge matching approach using Laplacian-Gaussian filtering for efficient scene representation in robotic applications, a paper that has garnered 24 citations. He also developed 3D measurement techniques for flexible objects, enabling advances in factory automation tasks such as cable handling. More recently, Okuda has made significant strides in motion planning for complex robotic platforms, particularly tractor-trailer mobile robots (TTMRs). His configuration-aware model predictive motion planning frameworks address strict obstacle avoidance in narrow environments using polygonal-shape constraints — work that has attracted up to 12 citations. His research further extends to four-wheel independent drive and steering vehicles and omni-directional tractor systems, broadening autonomous navigation capabilities. Okuda has also advanced human-robot interaction, investigating impedance-based manual control modes for autonomous mobile robots (AMRs) in manufacturing settings and developing eHMI-based communication strategies to improve AMR-pedestrian cooperation. His recent reinforcement learning work explores compassionate, human-aware motion planning in shared spaces. Across these diverse contributions, Okuda's research consistently bridges theoretical rigor with practical industrial application.
Research Focus
Key Achievements
Top Papers
- 1Three-level broad-edge matching based real-time robot vision24 citations · 2002
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- 93D measurement of flexible objects by robust motion stereo2 citations · 2007
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