Yoshiaki Jitsukawa
Papers
5
Total Citations
27
H-Index
3
About
Yoshiaki Jitsukawa is a researcher specializing in autonomous robotics, decision-making under uncertainty, and resource-constrained visual perception. His major contributions center on developing computationally efficient methods for real-time robot control, particularly through the real-time QMDP (Quality-Based Markov Decision Process) framework. This approach enables robots with limited processing power and sensors to make fast, optimal decisions in dynamic environments, as demonstrated in his work on soccer robot goalkeeping tasks. Jitsukawa also advanced image-based localization and similarity detection using Discrete Wavelet Transform (DWT), allowing robots like AIBO to navigate and recognize objects with minimal computational resources. His most cited paper, "Image similarity based on Discrete Wavelet Transform for robots with low-computational resources" (2010, 14 citations), highlights his focus on practical, low-cost solutions. Despite modest citation counts, his work addresses fundamental challenges in autonomous systems—balancing real-time performance with uncertain state estimation—making it valuable for students and researchers in robotics, computer vision, and embedded AI. Jitsukawa’s research bridges theory and application, offering scalable techniques for robots operating in unpredictable, real-world settings.
Research Focus
Key Achievements
Top Papers
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- 4Improvement of Color Recognition Using Colored Objects3 citations · 2006
- 5Toward Image-Based Localization for AIBO Using Wavelet Transform2 citations · 2007