Skid steering vehicles rely on tracks slipping to perform turning maneuvers. In this context, the estimation of the right amount of slip turns out to be significant to correctly perform precise movements. In a typical agricultural scenario, with rough terrain and narrow navigating spaces, a reliable slip estimation is crucial to perform safe motions. In this work, we propose a novel Gaussian Process approach to slip estimation in a tracked wheel robots by showing experimental results obtained from our prototype robotic platform.
Gentilini, L., Mengoli, D., Rossi, S., Marconi, L. (2022). Data-Driven Model Predictive Control for Skid-Steering Unmanned Ground Vehicles. 345 E 47TH ST, NEW YORK, NY 10017 USA : IEEE [10.1109/metroagrifor55389.2022.9964544].
Data-Driven Model Predictive Control for Skid-Steering Unmanned Ground Vehicles
Gentilini, Lorenzo;Mengoli, Dario
;Rossi, Simone;Marconi, Lorenzo
2022
Abstract
Skid steering vehicles rely on tracks slipping to perform turning maneuvers. In this context, the estimation of the right amount of slip turns out to be significant to correctly perform precise movements. In a typical agricultural scenario, with rough terrain and narrow navigating spaces, a reliable slip estimation is crucial to perform safe motions. In this work, we propose a novel Gaussian Process approach to slip estimation in a tracked wheel robots by showing experimental results obtained from our prototype robotic platform.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
