Zebracorn Labs

Here at the Zebracorns, we believe in learning and pushing boundaries. In pursuit of that, we have published papers, given talks, and have other tidbits of knowledge lying around. We hope you enjoy these as much as we do.

26 Sep 2019 -

Collaborative Whitepapers Using Overleaf

Overleaf generously agreed to provide us with a premium subscription which allows us to keep and share the source code of our whitepapers. In this paper, we want to express our gratitude for their generosity, explain our process for writing, editing, and publishing whitepapers, and talk about the features of Overleaf that we like the most.

11 Sep 2019 - Niall Mullane

Terabee Sensors for Robot Alignment

During the 2019 season, we received some distance sensors from Terabee which we have been experimenting with throughout the competition season. This whitepaper discusses the alignment algorithms that we developed using the Terabee sensors and how successful they were for the 2019 game.

15 Aug 2019 - Olivia Fugikawa, Adam Kosinski, Niall Mullane, Clara Wang, Kevin Jaget

ZebROS 1.1

In 2018, we wrote a comprehensive whitepaper explaining our groundbreaking work to introduce ROS to FRC. This year, we learned from last year's mistakes and challenges to write better code: code that was effectively organized for automation and took advantage of more of what ROS has to offer. We also made the huge step of transfering some CAN reads and writes to our NVIDIA Jetson TX2, requiring the setup of a second hardware interface. This whitepaper covers the biggest improvements that we made this year.

04 Jan 2019 - Christain Martens, Luke Cunningham, Bram Lovelace, Ty Sayman

2019 Offseason CAD Release

During offseason we focused on improving the swerve drive we used last year and the tank drive we designed last year in the case of obstacles. We’ve put them into a public GrabCAD space so feel free to peruse our files. Note: Most of the designs are either unfinished or won’t necessarily work in their current state.

06 Sep 2018 - Hans Khatri

ZebraVision 6.0: A Continuation of ROS for FRC

Zebravison 6.0 is a crucial development step towards the completion of a robust and dynamic codebase. Integration of ROS allows for automation and communication between systems, giving way to advanced developments in all features of sensors on the robot. The primary objective was to collect data and extract critical information about the position of the robot compared to other objects on the field. In other words, complete localization and environmental visualization in all aspects of the robot. This task, though expansive, has been finalized and polished off, leaving almost no robot-relative values unknown. Computer vision on The Zebracorns is closing the gap between the current standard of robotics and the goal of a fully functional, independent, and autonomous robot.

26 Jun 2018 - Anja Sheppard, Olivia Fugikawa, Niall Mullane, Ryan Greenblatt, Kevin Jaget

ZebROS 1.0 - ROS for FRC

In 2016, Team 900 wrote a neural network for detecting boulders. Last year, we implemented the Robot Operating System, ROS, into our vision code to facilitate communication between multiple processors. But this year, we’ve gone above and beyond what anyone thought we would be crazy enough to attempt. We transitioned our entire robot code -- including hardware control -- into ROS.

06 Jan 2018 - Mason Mitchell, Bram Lovelace

2018 Offseason CAD Release

During offseason we worked on a whole bunch of projects ranging from multiple different types of swerve drives and some west coast drives in preparation for the game. We’ve put them into a public GrabCAD space so feel free to peruse our files. Note: Most of the designs are either unfinished or won’t necessarily work in their current state.

05 Jan 2018 - Forrest Hurley

ZebraReality 0.1-0.3

Team 900 has lots of programmers. A positive cornucopia of programmers. Unfortunately, this often leaves us short on the mechanical side of things. Even after we get our robot built, the lack of a convenient practice field makes it difficult to test all of our robot’s functionality or train drivers. In 2017 we decided to program a world where reality isn't an issue. Our first year exploring VR ended relatively successfully. Our driver quickly adapted to a variety of field conditions and his clever maneuvering improved our score in several cases. We went through three iterations of the simulation, primarily rebuilding the robot drivetrains, and finally reached a stable model.