• DocumentCode
    495961
  • Title

    Online trajectory following with position based force/vision control

  • Author

    Alkkiomäki, Olli ; Kyrki, Ville ; Kälviäinen, Heikki ; Liu, Yong ; Handroos, Heikki

  • Author_Institution
    Dept. of Inf. Technol., Lappeenranta Univ. of Technol., Lappeenranta, Finland
  • fYear
    2009
  • fDate
    22-26 June 2009
  • Firstpage
    1
  • Lastpage
    6
  • Abstract
    Robot control in uncertain environments can greatly benefit from sensor based control. Visual sensing allows the robot to examine its surroundings and adapt to the environment. Force offers a complementary sensory modality allowing accurate measurements of local object shape when a tooltip is in contact with the object. In multimodal sensor fusion several sensors measuring different modalities are combined together to give more accurate estimate of the environment. We present a method which fuses force and vision in an extended Kalman filter (EKF). A hybrid force controller is then set up to follow a trajectory based on the estimate from the EKF. The estimate allows a simple proportional force control to track a continuous trajectory reliably, where an unfiltered visual measurement becomes unstable. Experiments verify that the method can increase the stability of control considerably.
  • Keywords
    Kalman filters; force control; image fusion; mobile robots; nonlinear filters; position control; robot vision; stability; uncertain systems; complementary sensory modality; extended Kalman filter; multimodal sensor fusion; object shape; online trajectory; position based force control; robot control; sensor based control; uncertain environment; unfiltered visual measurement; vision control; Force control; Force measurement; Force sensors; Fuses; Multimodal sensors; Robot control; Robot sensing systems; Sensor fusion; Shape measurement; Trajectory;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Advanced Robotics, 2009. ICAR 2009. International Conference on
  • Conference_Location
    Munich
  • Print_ISBN
    978-1-4244-4855-5
  • Electronic_ISBN
    978-3-8396-0035-1
  • Type

    conf

  • Filename
    5174726