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Manipulating Deformable Linear Objects: Sensor-Based Skills of Adjustment Motions for Vibration Reduction
Shigang Yue , Dominik Henrich

Abstract (english)
The vibration of a deformable linear object is often problematic during automatic handling by robot manipulators. However, humans can often handle and damp the vibration of deformable linear objects in a manner suitable to reduce acute vibration with simple human skill inspired strategies that consist of one or two adjustment motions. The adjustment motion is a simple open-loop motion that can be attached to the end of any arbitrary end-effector's trajectory. As an ordinary industrial robot's simple action, it has three periods, i.e., acceleration, constant speed, and deceleration period. it starts from a predicted time tightly close to a force/moment maximum. The predicted time for the adjustment action is generated automatically on-line based on the vibration rhythm and the data sensed by a force/torque sensor mounted on the robot's wrist. To find the matching point between the vibrational signal of the deformable object and a template, template matching techniques including cross-correlation and minimum squared error methods are used and compared. Experiments are conducted with an industrial robot to test the new skills under various conditions. The results demonstrate that an industrial robot could perform effective vibration reduction skills with simple strategies.

Publication data

Year: 2005
Publication date: 01. February 2005
Source: Journal of Robotic Systems 22(2), 67-85 (2005)
Project: RODEO


  TITLE             = "Manipulating Deformable Linear Objects: Sensor-Based Skills of Adjustment Motions for Vibration Reduction",
  AUTHOR            = "Yue, Shigang and Henrich, Dominik",
  YEAR              = "2005",
  JOURNAL           = "Journal of Robotic Systems 22(2), 67-85 (2005)",
  HOWPUBLISHED      = "\url{}",


Filename   Size   Language   Format
yue2005a.Manipulating.Deformable.Linear.Objects.SensorBased.Skills.of.Adjustment.Motions.for.Vibration.Reduction.pdf   709.8K   english   PDF   download preprint

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