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), August 2016.

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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",
   xxpages = "?-?",
   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.
   },
}

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