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A Fast, GPU-based Geometrical Placement Planner For Unknown Sensor-Modelled Objects And Placement Areas
Johannes Baumgartl , Dominik Henrich , Per Kaminsky

Abstract (english)
A Personal Robot should be able to handle un- known objects in unknown environments. For a manipulation task the question what to do with an object once it had been grasped is one of the most essential ones beside the grasping task itself. Moreover, the planning time should be at least as fast as the time the robot needs for its motions.
We propose a fast placement planner for sensor-modelled objects in complex environments. The planner computes a stable position and orientation for the object in the environment. The algorithm uses only geometric information, most notably no force or torque sensor is required. In particular, we introduce a novel approach regarding the pose computation.
By means of experiments with various household objects the robustness and performance are validated. Further on, we compare our approach with a pose computation using a physics simulation framework.

Publication data

Year: 2014
Publication date: 01. June 2014
Editor: IEEE
Place: Hong Kong
Source: International Conference on Robotics and Automation (ICRA 2014)
Project: PAP
Keywords (deutsch): Robotik
Keywords (english): manipulation skills , on-line algorithms , parallel processing , simulation , vision
Referrer: http://www.ai3.uni-bayreuth.de/resypub/?mode=pub_show&pub_ref=baumgartl2014c

BibTeX

@ARTICLE{baumgartl2014c,
  TITLE             = "A Fast, GPU-based Geometrical Placement Planner For Unknown Sensor-Modelled Objects And Placement Areas",
  AUTHOR            = "Baumgartl, Johannes and Henrich, Dominik and Kaminsky, Per",
  YEAR              = "2014",
  EDITOR            = "IEEE",
  JOURNAL           = "International Conference on Robotics and Automation (ICRA 2014)",
  HOWPUBLISHED      = "\url{http://www.ai3.uni-bayreuth.de/resypub/?mode=pub_show&pub_ref=baumgartl2014c}",
}

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baumgartl2014c.A.Fast.GPUbased.Geometrical.Placement.Planner.For.Unknown.SensorModelled.Objects.And.Placement.Areas.pdf   3.8M   english   PDF   download preprint


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