The Zebracorns continue to use the Robot Operating System (ROS) as the foundation for all of our robot code, for the seventh year in a row. During the 2023 season, we began to harness ROS in a way we hadn't before to gain a competitive advantage through streamlined code and ROS-based autonomous actions. We also had an extremely reliable robot; code ran successfully in 98% of our matches. Our work with ROS won us three Innovation in Control awards, giving us district points needed to qualify for the World Championship. In this paper, we cover the highlights of our work and what we've learned over the past few years, as well as the details of our 2023 robot's software.
In this paper, we cover our upgraded process for Ethernet switching on our robots. We cover the general rules we try to live by when it comes to the radio and adjacent components, the legal ways to connect and power the radio, and the assembly of the switch and the power regulator.
Written as an updated version of our 2018 paper, this paper outlines our process for assembling our batteries and explains the reasoning behind our improvements. We also cover topics such as disassembly, disposal, safety, and reliable connections to robot components.
During the 2023 FIRST Robotics Competition Season, we developed an intake mechanism that successfully intaked 2 separate game pieces. We also developed testing procedures to identify intake mechanism failures and their severity. In this paper, we outline the team's approach through the season.
Our regular pre-season posting
Our regular pre-season posting
Upon the occasion of the Global Innovation Competition, we were asked if we would like to share a regional recipe. Many of The Zebracorns enjoy baking so we were thrilled to be asked so that we could share our recipe for (Zebra)Cornbread, a true southern classic and something we hope others will enjoy.
During the past few years, we have focused on developing robust sensing and inference systems to accelerate our progress towards a fully autonomous robot. The ability to estimate the robot’s absolute position on the field is critical to achieving this goal. During the 2020 season, we made groundbreaking strides in this vein, developing systems to make our robot more environment-aware. In this paper, we introduce two systems: a neural network-based object detector and a particle filter localization system. We believe that neural networks are the future of computer vision in FRC; to help foster innovation in the FRC community, we release our dataset from this season.
Over the past four years, the Zebracorns have created a modular design philosophy and development model based on the Robot Operating System (ROS). Last year, we focused on polishing the core elements and laying the foundation of our robot code base. During the 2020 season, we were able to leverage the advantages of this system as we continue to build upon our previous work. Many of this year’s improvements centered around reliability of the robot itself, as well as driver-robot interaction. Other improvements are new features that bring us closer to the goal of a fully autonomous robot.
A very real whitepaper about the sensing of colors utilizing neural networks in FRC