Cyberworks Robotics

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 will work 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 will also work 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. This student team will work to: - Identify of navigational confounds in a complex large scale indoor/outdoor environment - Apply AWS Robomaker cloud connectivity, monitoring, authentication and software management - Apply Point Cloud Obstacle Cluster Velocity Tracking - Apply graceful Motion based Navigation (U Michigan papers) The outcome this student team will work to achieve is graceful robust autonomous wheelchair navigation in large-scale real-world environments at an affordable price using the Cyberworks Autonomous Navigation Stack