Technical Report

Intention Driven Assistive Wheelchair Navigation

  • March 2008
  • Report number: University of Hertfordshire Technical Report 470, UK, pp. 71-77.
  • Affiliation: "Robotic Helpers: User Interaction, Interfaces and Companions in Assistive and Therapy Robotics", a workshop at ACM/IEEE Human Robot Interaction (HRI)
Authors:
Tarek Taha at Dubai Futue Labs
Tarek Taha
  • Dubai Futue Labs
Jaime Valls Miro at University of Technology Sydney
Jaime Valls Miro
Gamini Dissanayake
Gamini Dissanayake
Gamini Dissanayake
  • This person is not on ResearchGate, or hasn't claimed this research yet.
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Abstract

This paper presents an intelligent decision-making agent to assist wheelchair users in their daily navigation activities. The system has the ability to predict the users' intended destination at a larger scale, that of a typical office or home arena. This system relies on minimal user input - obtained from a standard wheelchair joystick - in conjunction with a learned Partially Observable Markov Decision Process (POMDP), to estimate and subsequently aid in driving the user to the destination. The projection is constantly being updated, allowing for true user-platform integration. This shifts users' focus from fine motor-skilled control to coarse guidance, broadly intended to convey intention. Successful simulation and experimental results on a real automated wheelchair platform demonstrate the validity of the approach.

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... As the interest grew in HAN, robotic wheelchairs were actively improving, and several works were published during this period. The works presented in [Demeester 2008, Taha 2008, Vanhooydonck 2010] studied the ideas of shared control and user intent recognition. Demeester et al. [Demeester 2008] presented a user-adapted plan recognition and shared control of wheelchair using the Bayesian approach and POMDPs by using explicit communication, whereas Vanhooydonck et al. [Vanhooydonck 2010] used artificial neural networks to learn the user intent implicitly. ...
... Demeester et al. [Demeester 2008] presented a user-adapted plan recognition and shared control of wheelchair using the Bayesian approach and POMDPs by using explicit communication, whereas Vanhooydonck et al. [Vanhooydonck 2010] used artificial neural networks to learn the user intent implicitly. Taha et al. [Taha 2008] used POMDP based approach again, but instead of aiming at a semi-autonomous control, the system predicts the user's intended goal in a given environment and takes the human there with minimal input from the joystick. Intention detection could be of use in autonomous vehicles as well. ...
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