Mauro Mancini
Papers
2
Total Citations
7
H-Index
1
About
Mauro Mancini is a robotics and control systems researcher whose work focuses on autonomous navigation for agricultural and aerial vehicles. His primary research areas include Guidance, Navigation and Control (GNC) for Unmanned Ground Vehicles (UGVs) and Unmanned Aerial Vehicles (UAVs), with a particular emphasis on precision agriculture applications. Mancini’s most cited work, “Adaptive Sliding Mode Control with Artificial Potential Field for Ground Robots in Precision Agriculture” (2023, 6 citations), addresses a critical challenge in agricultural robotics: reliable navigation in GPS-denied environments. By combining adaptive sliding mode control with artificial potential fields, he developed a robust framework for autonomous UGV operation when satellite signals are weak or unavailable. His more recent work, “Comparison of NMPC and GPU-Parallelized MPPI for Real-Time UAV Control on Embedded Hardware” (2025), advances real-time control strategies for drones operating in complex environments, comparing Nonlinear Model Predictive Control with Model Predictive Path Integral methods. Mancini’s contributions are particularly valuable for the agricultural sector, where his control strategies enable more reliable autonomous operations in challenging field conditions, potentially reducing the need for human intervention in precision farming tasks.
Research Focus
Key Achievements
Top Papers
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