Hanana Furukawa
Papers
1
Total Citations
3
H-Index
1
About
Hanana Furukawa is a robotics researcher whose work focuses on advancing automated manipulation and grasping systems for industrial applications. Her primary research areas include robotic grasping, computer vision, and optimization algorithms for manufacturing environments. Furukawa's most notable contribution is her 2019 paper, "Grasping Position Detection Using Template Matching and Differential Evolution for Bulk Bolts," which addresses the challenge of enabling robots to efficiently pick randomly stacked bolts from bulk containers. Rather than relying on computationally intensive deep learning methods that require extensive training data and preparation time, she proposed a novel approach combining template matching with differential evolution optimization. This method allows for rapid detection of optimal grasping positions without the need for pre-trained models, making it particularly valuable for small-scale manufacturing settings where data collection is impractical. While her citation count of 3 reflects the specialized nature of this work, her research represents a practical, efficient alternative to deep learning approaches in industrial robotics, demonstrating how classical optimization techniques can solve real-world automation challenges with minimal computational overhead.
Research Focus
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Top Papers
- 1