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About

Davide Carminati is a robotics researcher whose work focuses on the Guidance, Navigation, and Control (GNC) of autonomous ground vehicles, with a particular emphasis on precision agriculture. His research addresses the critical challenge of reliable robot navigation in GPS-denied agricultural environments, where traditional satellite-based positioning often fails. Carminati’s major contributions include the development of an adaptive sliding mode controller integrated with artificial potential fields, enabling safe and effective autonomous navigation for unmanned ground vehicles (UGVs) in complex field conditions. He has also advanced system identification techniques, pioneering the use of Gaussian process regression (GPR) models to accurately simulate and control nonlinear robotic systems. His work on data-driven identification methods has improved the fidelity of simulation environments, allowing for more robust controller design. With his most-cited paper, “Adaptive Sliding Mode Control with Artificial Potential Field for Ground Robots in Precision Agriculture,” accumulating 6 citations, Carminati is establishing a strong foundation for next-generation agricultural robotics. His research is particularly notable for bridging the gap between theoretical control systems and practical, real-world deployment in challenging outdoor settings.