Using machine learning to blend human and robot controls for assisted wheelchair navigation

Aditya Goil, Matthew Derry, Brenna Dee Argall

Research output: Chapter in Book/Report/Conference proceedingConference contribution

28 Scopus citations

Abstract

This work presents an algorithm for collaborative control of an assistive semi-autonomous wheelchair. Our approach is based on a statistical machine learning technique to learn task variability from demonstration examples. The algorithm has been developed in the context of shared-control powered wheelchairs that provide assistance to individuals with impairments that affect their control in challenging driving scenarios, like doorway navigation. We validate our algorithm within a simulation environment, and find that with relatively few demonstrations, our approach allows for safe traversal of the doorway while maintaining a high level of user control.

Original languageEnglish (US)
Title of host publication2013 IEEE 13th International Conference on Rehabilitation Robotics, ICORR 2013
DOIs
StatePublished - Dec 31 2013
Event2013 IEEE 13th International Conference on Rehabilitation Robotics, ICORR 2013 - Seattle, WA, United States
Duration: Jun 24 2013Jun 26 2013

Other

Other2013 IEEE 13th International Conference on Rehabilitation Robotics, ICORR 2013
CountryUnited States
CitySeattle, WA
Period6/24/136/26/13

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering
  • Rehabilitation
  • Medicine(all)

Fingerprint Dive into the research topics of 'Using machine learning to blend human and robot controls for assisted wheelchair navigation'. Together they form a unique fingerprint.

Cite this