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Autonomous Wheelchair Indoor-Outdoor Navigation System through Accessible Routes

Published:29 June 2021Publication History

ABSTRACT

Persons with disabilities who use power wheelchairs often have difficulty finding accessible routes to their destination from their outdoor location into the desired building or from their indoor location into another destination. This challenge can be alleviated through sensory information and preloaded map of the building and its surrounding outdoor areas. In this work, a power wheelchair system is integrated with a sensory suite and an autonomous control module to navigate the wheelchair in autonomous mode. A landmark-based autonomous navigation system using Quick Response (QR) code and Global Positioning System (GPS) technology is used for automatic communication between the power wheelchair and various locations in a designated building. For the outdoor navigation system, Autonomous Wheelchair Indoor-Outdoor Navigation System (AWI-ONS) relies on GPS signals and Google Maps. For indoor navigation, QR codes are placed in several key locations inside and outside the building, such as parking lots and garages, doorways, offices, bathrooms, stores, elevators, accessible entrances, and passageways. When scanned by the onboard camera, the AWI-ONS downloads the building floorplan from the Firebase server for indoor navigation. The floor plan with QR code information generates a topological map that is made available to the user through a touch screen user interface (UI). The user can select the destination, and AWI-ONS will generate the most viable and accessible path to the desired location using modified Breadth-First Search (BFS) algorithm. The AWI-ONS is fitted with Ultrasound-based obstacle avoidance system that is designed to avoid a possible collision while navigating towards the destination.

References

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  • Published in

    cover image ACM Other conferences
    PETRA '21: Proceedings of the 14th PErvasive Technologies Related to Assistive Environments Conference
    June 2021
    593 pages
    ISBN:9781450387927
    DOI:10.1145/3453892

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    • Published: 29 June 2021

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