Data-Driven Model Predictive Control for Skid-Steering Unmanned Ground Vehicles

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...

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Bibliographic Details
Published in2022 IEEE Workshop on Metrology for Agriculture and Forestry (MetroAgriFor) pp. 80 - 85
Main Authors Gentilini, Lorenzo, Mengoli, Dario, Rossi, Simone, Marconi, Lorenzo
Format Conference Proceeding
LanguageEnglish
Published IEEE 03.11.2022
Subjects
Atmospheric measurements
Estimation
Gaussian Process
Gaussian processes
Learning & Control
Mathematical models
Model Predictive Control
Particle measurements
Tracking
Turning
Unmanned Ground Vehicle
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Summary: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.
DOI:10.1109/MetroAgriFor55389.2022.9964544