• DocumentCode
    250237
  • Title

    Contact event detection for robotic oil drilling

  • Author

    Wu, X. Alice ; Burkhard, Natalie ; Heyneman, Barrett ; Valen, Roald ; Cutkosky, Mark

  • Author_Institution
    Center for Design Res., Stanford Univ., Stanford, CA, USA
  • fYear
    2014
  • fDate
    May 31 2014-June 7 2014
  • Firstpage
    2255
  • Lastpage
    2261
  • Abstract
    To ensure safe and reliable operation in a robotic oil drilling system, it is essential to detect contact events such as impacts and slips between end-effectors and workpieces. In this challenging application, where high forces are used to manipulate heavy metal pipes in noisy environments, acoustic emissions (AE) sensors offer a promising contact sensing solution. Real-time AE signal features are used to create a multinomial contact event classifier. The sensitivity of signal features to a variety of contact events including two types of slip is presented. Results indicate that the classifier is able to robustly and dynamically classify contact events with >90% accuracy using a small set of AE signal features.
  • Keywords
    acoustic emission; end effectors; feature extraction; industrial robots; oil drilling; pipes; reliability; safety; sensors; signal classification; AE sensors; acoustic emissions sensors; contact event detection; contact sensing solution; end-effectors; heavy metal pipes; impacts; multinomial contact event classifier; noisy environments; real-time AE signal features; reliable operation; robotic oil drilling system; safe operation; signal features sensitivity; slips; workpieces; Accuracy; Grippers; Noise; Robot sensing systems; Steel; acoustic emissions; contact sensing; manipulation; slip;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Robotics and Automation (ICRA), 2014 IEEE International Conference on
  • Conference_Location
    Hong Kong
  • Type

    conf

  • DOI
    10.1109/ICRA.2014.6907171
  • Filename
    6907171