Yasuhide Hirohata
Papers
1
Total Citations
9
H-Index
1
About
Yasuhide Hirohata is a researcher in robotics and control systems, with a focus on safe autonomous navigation and learning-based control. His key research areas include coverage control, safety-critical systems, and the integration of machine learning with control barrier functions. In his most cited work, "Safe Persistent Coverage Control with Control Barrier Functions Based on Sparse Bayesian Learning" (2022, 9 citations), Hirohata proposes a novel algorithm that enables robots to explore unknown environments while ensuring safety by learning obstacle constraints from sensor data. By training a sparse Bayesian classifier to estimate collision probabilities, his approach bridges the gap between data-driven perception and formal safety guarantees. This contribution is significant for applications in search-and-rescue, environmental monitoring, and autonomous exploration, where robots must operate reliably in unpredictable settings. Though early in his career, Hirohata’s work demonstrates a strong commitment to developing theoretically grounded, practical solutions for real-world robotic systems. His research holds promise for advancing the field of safe autonomy, particularly in scenarios requiring persistent coverage under uncertainty.
Research Focus
Key Achievements
Top Papers
- 1