Hiroka Zushi
Papers
2
Total Citations
40
H-Index
2
About
Hiroka Zushi is a leading researcher in autonomous mobile robotics, with a primary focus on intelligent local path planning and navigation in dynamic, congestion-prone environments. Her most impactful work introduces the **Dynamic Window Approach with Q-Learning (DQDWA)**, a groundbreaking method that integrates reinforcement learning to adaptively adjust weight coefficients in real time based on environmental situations. This innovation allows mobile robots to make smarter, context-aware decisions—balancing speed, safety, and obstacle avoidance—far beyond what traditional static-parameter approaches can achieve. Her seminal 2023 paper on this topic has already garnered **38 citations**, reflecting its rapid influence on the field. Zushi’s research directly addresses the growing need for robust, autonomous navigation in industrial and service robotics, where robots must operate safely alongside humans and other moving agents. By combining classical control theory with modern machine learning, she has created a practical framework that improves both efficiency and adaptability. Her work is essential reading for anyone developing next-generation autonomous systems, and she continues to push the boundaries of how robots perceive and react to complex, crowded spaces.
Research Focus
Key Achievements
Top Papers
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- 2