Search

Title Author Year Keyword Project  include Videos
or and

Publication

A 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 possible unknown 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.
We propose a 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 configuration computation.
By means of experiments with various household objects the robustness and performance are validated. Further on, we compare our approach to a configuration computation using a physics simulation framework.

Publication data

Year: 2013
Publication date: 13. December 2013
Editor: IEEE
Place: Shenzhen, China
Source: International Conference on Robotics and Biomimetics (ROBIO)
Project: PAP
Keywords (deutsch): Robotik
Keywords (english): manipulation skills , on-line algorithms
Referrer: http://www.ai3.uni-bayreuth.de/resypub/?mode=pub_show&pub_ref=baumgartl2013.12a

BibTeX

@ARTICLE{baumgartl2013.12a,
  TITLE             = "A Geometrical Placement Planner For Unknown Sensor-Modelled Objects And Placement Areas",
  AUTHOR            = "Baumgartl, Johannes and Henrich, Dominik and Kaminsky, Per",
  YEAR              = "2013",
  EDITOR            = "IEEE",
  JOURNAL           = "International Conference on Robotics and Biomimetics (ROBIO)",
  HOWPUBLISHED      = "\url{http://www.ai3.uni-bayreuth.de/resypub/?mode=pub_show&pub_ref=baumgartl2013.12a}",
}

Download

Filename   Size   Language   Format
baumgartl2013.12a.A.Geometrical.Placement.Planner.For.Unknown.SensorModelled.Objects.And.Placement.Areas.pdf   3.5M   english   PDF   download preprint


© LS AI3 Uni-Bayreuth