Accéder directement au contenu Accéder directement à la navigation
Article dans une revue

Slippage prediction for off-road mobile robots via machine learning regression and proprioceptive sensing

Abstract : This paper presents a new approach for predicting slippage associated with individual wheels in off-road mobile robots. More specifically, machine learning regression algorithms are trained considering proprioceptive sensing. This contribution is validated by using the MIT single-wheel testbed equipped with an MSL spare wheel. The combination of IMU-related and torque-related features outperforms the torque-related features only. Gaussian process regression results in a proper trade-off between accuracy and computation time. Another advantage of this algorithm is that it returns the variance associated with each prediction, which might be used for future route planning and control tasks. The paper also provides a comparison between machine learning regression and classification algorithms.
Type de document :
Article dans une revue
Liste complète des métadonnées

Littérature citée [28 références]  Voir  Masquer  Télécharger

https://hal.univ-grenoble-alpes.fr/hal-01904487
Contributeur : Mirko Fiacchini <>
Soumis le : mardi 6 novembre 2018 - 11:26:51
Dernière modification le : jeudi 9 juillet 2020 - 17:02:03
Archivage à long terme le : : jeudi 7 février 2019 - 13:09:55

Fichier

TransRobot18.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

Collections

Citation

Ramon Gonzalez, Mirko Fiacchini, Karl Iagnemma. Slippage prediction for off-road mobile robots via machine learning regression and proprioceptive sensing. Robotics and Autonomous Systems, Elsevier, 2018, 105, pp.85 - 93. ⟨10.1016/j.robot.2018.03.013⟩. ⟨hal-01904487⟩

Partager

Métriques

Consultations de la notice

121

Téléchargements de fichiers

341