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

A real-time technique for positioning a wheelchair-mounted robotic arm for household manipulation tasks


Cite as:

P. Abolghasemi, R. Rahmatizadeh, A. Behal, and L. Bölöni. A real-time technique for positioning a wheelchair-mounted robotic arm for household manipulation tasks. In Workshop on Artificial Intelligence Applied to Assistive Technologies and Smart Environments (ATSE-16) at AAAI-2016, February 2016.

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Abstract:

Wheelchair mounted robotic arms can help people with disabilities perform their activities of daily living (ADL). The autonomy of such a system can range from full manual control (both wheelchair and robotic arm controlled by the human) to fully autonomous (with both the wheelchair and the robotic arm under autonomous control). Many ADLs require the robot to pick up an object from a cluttered environment - such as a glass of water from a table where several other objects exist. In this paper, we concentrate on the task of finding the optimal position of the base of the robotic arm (which is normally a rigid point on the wheelchair) such that the end effector can easily reach the target (regardless whether this is done through human or robot control). We introduce the ease-of-reach score ERS, a metric quantifying the preferences for the positioning of the base. As the brute force computation of ERS is computationally expensive, we propose an approach of estimating the ERS through a mixture of Gaussians. The parameters of the component Gaussians are learned offline and depend on the nature of the environment such as properties of the the obstacles. Simulation results show that the estimated ERS closely matches the actual value and the speed of estimation is fast enough for real-time operation.

BibTeX:

@inproceedings{Abolghasemi-2016-ATSE,
   title = "A real-time technique for positioning a wheelchair-mounted robotic arm for household manipulation tasks",
   author = "P. Abolghasemi and R. Rahmatizadeh and A. Behal and L. B{\"o}l{\"o}ni",
   booktitle = "Workshop on Artificial Intelligence Applied to Assistive Technologies and Smart Environments (ATSE-16) at AAAI-2016",
   year = "2016",
   month = "February",
   abstract = {
   Wheelchair mounted robotic arms can help people with disabilities
   perform their activities of daily living (ADL). The autonomy of such
   a system can range from full manual control (both wheelchair and
   robotic arm controlled by the human) to fully autonomous (with both
   the wheelchair and the robotic arm under autonomous control). Many
   ADLs require the robot to pick up an object from a cluttered
   environment - such as a glass of water from a table where several
   other objects exist. In this paper, we concentrate on the task of
   finding the optimal position of the base of the robotic arm (which is
   normally a rigid point on the wheelchair) such that the end effector
   can easily reach the target (regardless whether this is done through
   human or robot control). We introduce the ease-of-reach score ERS, a
   metric quantifying the preferences for the positioning of the base.
   As the brute force computation of ERS is computationally expensive,
   we propose an approach of estimating the ERS through a mixture of
   Gaussians. The parameters of the component Gaussians are learned
   offline and depend on the nature of the environment such as
   properties of the the obstacles. Simulation results show that the
   estimated ERS closely matches the actual value and the speed of
   estimation is fast enough for real-time operation.
   }
}

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