Autonomous Self-Driving Wheelchair

Autonomous Wheelchairs increase freedom and ease of mobility for the most vulnerable peoples in society. Large-scale, campus-wide, autonomous navigation of a power wheelchair faces numerous corner case confounds ranging from loss of localization due to feature-sparsity to human motion sickness. This student team worked to identify and address corner cases that allow for robust persistent navigation over vast indoor and outdoor regions within the UW campus and fleet integration to the cloud for remote monitoring, user authentication, and over-the-air updates. Low hardware cost is essential to mass adoption of such technology. Therefore this student team also worked to focus on use of inexpensive but cutting-edge technologies, such as monocular Neural SLAM and Dense Optical FlowML Behavior Cloning, as a means to reduce or eliminate reliance on expensive LiDAR based visual SLAM. Ultimately, this student team worked to create graceful, robust, autonomous wheelchair navigation in large-scale real-world environments at an affordable price using the Cyberworks Autonomous Navigation Stack.

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