Naoya Mukai
Papers
1
Total Citations
9
H-Index
1
About
Naoya Mukai is a robotics researcher whose work centers on autonomous mobile manipulation, with a particular focus on dual-arm systems, path planning, and real-world object recognition. His most cited study, "Application of Object Grasping Using Dual-Arm Autonomous Mobile Robot—Path Planning by Spline Curve and Object Recognition by YOLO—" (2023, 9 citations), addresses a core challenge in service robotics: enabling a robot to autonomously collect scattered trash—such as bottles, cans, and bento boxes—within a time limit. Mukai’s contribution lies in integrating YOLO-based deep learning for object detection with spline curve path planning, allowing a dual-arm robot to efficiently navigate to and grasp target items. This work was developed in the context of the Nakanoshima Robot Challenge, a competitive benchmark for autonomous trash-collection systems. While his citation count is still growing, Mukai’s research demonstrates a practical, systems-level approach to deploying AI and robotics in unstructured environments. His work is particularly relevant for students and researchers interested in the intersection of computer vision, motion planning, and real-world robot autonomy.
Research Focus
Key Achievements
Top Papers
- 1