R. Rahmatizadeh, P. Abolghasemi, A. Behal, and L. Bölöni

Real-time placement of a wheelchair-mounted robotic arm


Cite as:

R. Rahmatizadeh, P. Abolghasemi, A. Behal, and L. Bölöni. Real-time placement of a wheelchair-mounted robotic arm. In IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN-2016), pp. 1032–1037, August 2016.

Download:

Download Video 

Abstract:

Picking up an object with a wheelchair mounted robotic arm can be decomposed into a wheelchair navigation task designed to position the robotic arm such that the object is ``easy to reach'', and the actual grasp performed by the robotic arm. A convenient definition of the notion of ease of reach can be given by creating a score (ERS) that relies on the number of distinct ways the object can be picked up from a given location. Unfortunately, the accurate calculation of ERS must rely on repeating the path planning process for every candidate position and grasp type, in the presence of obstacles. In this paper we use the bootstrap aggregation over hand-crafted, domain specific features to learn a model for the estimation of ERS. In a simulation study, we show that the estimated ERS closely matches the actual value and the speed of estimation is fast enough for real-time operation, even in the presence of a large number of obstacles in the scene.

BibTeX:

@inproceedings{Abolghasemi-2016-ROMAN,
title = "Real-time placement of a wheelchair-mounted robotic arm",
author = "R. Rahmatizadeh and P. Abolghasemi and A. Behal and L. B{\"o}l{\"o}ni",
booktitle = "IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN-2016)",
month = "August",
year = "2016",
doi = "10.1109/ROMAN.2016.7745235",
pages = "1032-1037",
abstract = {
  Picking up an object with a wheelchair mounted robotic arm can be
  decomposed into a wheelchair navigation task designed to position the
  robotic arm such that the object is ``easy to reach'', and the actual
  grasp performed by the robotic arm. A convenient definition of the
  notion of ease of reach can be given by creating a score (ERS) that
  relies on the number of distinct ways the object can be picked up
  from a given location. Unfortunately, the accurate calculation of ERS
  must rely on repeating the path planning process for every candidate
  position and grasp type, in the presence of obstacles. In this paper
  we use the bootstrap aggregation over hand-crafted, domain specific
  features to learn a model for the estimation of ERS. In a simulation
  study, we show that the estimated ERS closely matches the actual
  value and the speed of estimation is fast enough for real-time
  operation, even in the presence of a large number of obstacles in the
  scene.
 },
}

Generated by bib2html.pl (written by Patrick Riley, Lotzi Boloni ) on Thu Sep 26, 2019 13:02:58