• About
    • Login
    View Item 
    •   Institutional Repository Home
    • Electronic Theses and Dissertations
    • Electronic Theses and Dissertations
    • View Item
    •   Institutional Repository Home
    • Electronic Theses and Dissertations
    • Electronic Theses and Dissertations
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

    All of Institutional RepositoryCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsDepartmentThis CollectionBy Issue DateAuthorsTitlesSubjectsDepartment

    My Account

    LoginRegister

    Extraction of Salient Features from Sensory-Motor Sequences for Mobile Robot Navigation

    Peng, Jian
    : https://etd.library.vanderbilt.edu/etd-04062004-164409
    http://hdl.handle.net/1803/12024
    : 2004-04-13

    Abstract

    This dissertation presents a method to extract features salient to a mobile robot navigation task in a specific environment. The extraction process is bootstrapped by a human operator’s tele-operation and is based on the sensory-motor coordination principle. Salient feature extraction consists of three steps: tele-operation, offline association, and evaluation. First, the mobile robot is tele-operated in an environment along a path several times. All sensory data and motor drive commands are recorded. Then these recorded sensory-motor sequences are partitioned into episodes according to the changes in the motor commands. Salient features are then extracted by using two statistical criteria: consistency and correlation with the motor commands within an interval around the episode boundaries. Finally, these features are used to drive the robot in the learned environment. Two sets of experiments, in both indoor and outdoor environments, were performed. The results endorsed this methodology.
    Show full item record

    Files in this item

    Icon
    Name:
    final_electrical.pdf
    Size:
    3.005Mb
    Format:
    PDF
    View/Open

    This item appears in the following collection(s):

    • Electronic Theses and Dissertations

    Connect with Vanderbilt Libraries

    Your Vanderbilt

    • Alumni
    • Current Students
    • Faculty & Staff
    • International Students
    • Media
    • Parents & Family
    • Prospective Students
    • Researchers
    • Sports Fans
    • Visitors & Neighbors

    Support the Jean and Alexander Heard Libraries

    Support the Library...Give Now

    Gifts to the Libraries support the learning and research needs of the entire Vanderbilt community. Learn more about giving to the Libraries.

    Become a Friend of the Libraries

    Quick Links

    • Hours
    • About
    • Employment
    • Staff Directory
    • Accessibility Services
    • Contact
    • Vanderbilt Home
    • Privacy Policy