Ryuki Higuchi

Papers

Total Citations

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About

Ryuki Higuchi is a researcher in autonomous robotics, focusing on enabling mobile robots to navigate and understand their environments without pre-built maps. His primary research areas include computer vision, convolutional neural networks (CNNs), and path planning for autonomous systems. Higuchi's major contributions center on developing methods for road and intersection detection using deep learning, as well as path extraction techniques that allow robots to derive navigable routes from raw sensor data. His most-cited work, "Road and Intersection Detection Using Convolutional Neural Network" (2020, 6 citations), presents a CNN-based approach that simultaneously detects roads and intersections, enabling robots to autonomously determine movement direction and count turns to reach destinations. In "Path Extraction for Autonomous Mobile Robot Using Skeletonization" (2021, 2 citations), he addresses the challenge of path planning in unmapped environments by using skeletonization algorithms to derive paths from environmental data, eliminating the need for manual waypoint setting. While his citation counts are modest, Higuchi's work represents foundational steps toward more autonomous and adaptive robotic navigation systems, particularly in unstructured or unknown environments.