Reo Sugata
Papers
2
Total Citations
2
H-Index
1
About
Reo Sugata is a robotics researcher focused on advancing autonomous navigation for mobile robots, with a particular emphasis on motion planning and obstacle avoidance in dynamic environments. His work addresses the critical challenge of enabling robots to navigate safely among multiple obstacles, including unpredictable entities like pedestrians. Sugata’s major contributions include the development of an open-space-based motion planner that enhances a robot’s ability to avoid collisions in cluttered settings, moving beyond traditional end-to-end approaches. He has also pioneered the integration of optical flow images into motion planning, allowing robots to perceive and react to the moving direction of obstacles—a key advancement for real-world deployment. While his most-cited papers, published in 2024 and 2025, currently hold 1 citation each, they represent foundational steps in a rapidly evolving field. Sugata’s work is notable for its practical focus on combining convolutional neural networks with multimodal inputs, such as RGB and depth data, to improve robotic perception and decision-making. His research is particularly relevant for students and engineers developing autonomous systems for crowded or unpredictable environments, offering innovative solutions that bridge computer vision and robotics.
Research Focus
Key Achievements
Top Papers
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- 2