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

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 whe...

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Bibliographic Details
Main Authors: Goil, Aditya, Derry, Matthew, Argall, Brenna D.
Format: Conference Proceeding
Language:English
Subjects:
Aerospace electronics
Algorithms
Artificial Intelligence
Equipment Design
Man-Machine Systems
Mathematical model
Mobile robots
Navigation
Robot sensing systems
Robotics - instrumentation
Wheelchairs
Online Access:Request full text
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Description
Summary: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.
ISSN:1945-7898
1945-7901
DOI:10.1109/ICORR.2013.6650454