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Spring 2018: Zack Allen, Nicholas Weiland, Jordan Domsky, Alex Martin
Summer 2019: Maddie Brower, Nathan Moore
Fall 2019: Jeffrey Banghart, Nathan Chan, Tyree Mitchell, Maddie Brower
Spring/Summer 2020: Tyree Mitchell, Maddie Brower, Joshua Griffin
Spring 2022: Jacob Bringham, Jacob McClaskey, Matthew Dim, Amber Oliver, Matty Wolfson
The Spring 2018 course focused on building the foundation of the Autonomous Navigation code from the ground up.
In Summer of 2019, Maddie Brower and Nathan Moore focused on reorganizing the existing navigation code, experimenting with Autoware, mapping, and improving simulator models.
In Fall 2019, the core focus was to integrate Autoware localization, integrate machine learning applications from a machine learning team, integrate with new golf cart electronics, improve navigation code (e.g. Simplified calculations and launch files, improved node communication, better navigation planning, etc), and to implement basic obstacle avoidance.
Spring/Summner 2020 focus is to iron out many bugs from the Fall 2019 session, make any hardcoded systems flexible for a wider variety of situations and environments, implement more advanced solutions for things like planning, navigation, and obstacle avoidance.
In Spring 2022, the main area of focus was to improve obstacle detection within the LIDAR's blindspot. This was solved by adding a ZED2i and running point cloud and object detection algorithms to detect obstacles of all sizes. Within this semester, we also expanded the point cloud and routing files to include the entire campus.
- System Overview
- System Setup
- System Launch
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Setup System for new environments
- Collecting Data
- Map Building
- Path Building