Carlo Cernicchiaro
Papers
1
Total Citations
3
H-Index
1
About
Carlo Cernicchiaro is a researcher focused on autonomous navigation and control systems for robotic platforms, particularly in challenging maritime environments. His work centers on trajectory planning and obstacle avoidance for Unmanned Surface Vessels (USVs), with a special emphasis on autonomous sailboats—a domain where dynamic factors like wind and currents demand innovative solutions. His most-cited paper (2024, 3 citations) offers a rigorous comparison of Deep Reinforcement Learning (DRL) against classical methods like Artificial Potential Fields (APF) and A* with Proportional-Integral (PI) control for sailboat robot navigation. This study highlights his contribution to bridging modern machine learning techniques with traditional control theory, addressing the unique constraints of wind-powered vessels. While early in his career, Cernicchiaro’s work demonstrates a commitment to advancing autonomous maritime systems, providing foundational insights for future research in adaptive, real-time path planning under uncertainty. His research is particularly relevant for students and engineers exploring robust navigation in unpredictable environments.
Research Focus
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Top Papers
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