Safe, autonomous mobility in rough terrain is an important requirement for planetary exploration rovers. Knowledge of local terrain properties is critical to ensure a rover's safety on slopes and uneven surfaces. Visual features are often used to classify terrain; however, vision can be sensitive to lighting variations and other effects. This paper presents a method to classify terrain based on vibrations induced in the rover structure by wheel-terrain interaction during driving. This sensing mode is robust to lighting variations. Vibrations are measured using an accelerometer mounted on the rover structure. The classifier is trained using labeled vibration data during an offline learning phase. Linear discriminant analysis is used for online identification of terrain classes, such as sand, gravel, or clay. This approach has been experimentally validated on a laboratory testbed and on a four-wheeled rover in outdoor conditions.


    Access

    Access via TIB

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Vibration-based terrain classification for planetary exploration rovers


    Contributors:
    Brooks, C.A. (author) / Iagnemma, K. (author)

    Published in:

    IEEE Transactions on Robotics ; 21 , 6 ; 1185-1191


    Publication date :

    2005


    Size :

    7 Seiten, 28 Quellen




    Type of media :

    Article (Journal)


    Type of material :

    Print


    Language :

    English




    Terrain Classification and Classifier Fusion for Planetary Exploration Rovers

    Halatci, Ibrahim / Brooks, Christopher A. / Iagnemma, Karl | IEEE | 2007


    Visual, tactile, and vibration-based terrain analysis for planetary rovers

    Legnemma, K. / Brooks, C. / Dubowsky, S. | IEEE | 2004



    A terrain slope estimation scheme using infrared camera for planetary exploration rovers

    Watanabe, Satoshi / Otsu, Kyohei / Otsuki, Masatsugu et al. | IEEE | 2018


    Unsupervised classification of slip events for planetary exploration rovers

    Bouguelia, Mohamed-Rafik / Gonzalez, Ramon / Iagnemma, Karl et al. | Elsevier | 2017