https://github.com/team334/swervebase-ctre
A base project for future robots that has CTRE generated swerve drive code and PhotonVision AprilTag processing.
https://github.com/team334/swervebase-ctre
ctre frc java swerve
Last synced: 3 months ago
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A base project for future robots that has CTRE generated swerve drive code and PhotonVision AprilTag processing.
- Host: GitHub
- URL: https://github.com/team334/swervebase-ctre
- Owner: Team334
- License: other
- Created: 2024-11-14T00:21:44.000Z (7 months ago)
- Default Branch: main
- Last Pushed: 2025-01-18T03:18:30.000Z (5 months ago)
- Last Synced: 2025-03-05T02:17:49.972Z (3 months ago)
- Topics: ctre, frc, java, swerve
- Language: Java
- Homepage:
- Size: 3.71 MB
- Stars: 2
- Watchers: 4
- Forks: 1
- Open Issues: 5
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Metadata Files:
- Readme: README.md
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README
# SwerveBase-CTRE
[](https://github.com/Team334/SwerveBase-CTRE/actions/workflows/main.yml)A base project for future robots that has CTRE generated swerve drive code and Photon Vision AprilTag processing.
# Features
- Swerve drive code using CTRE's swerve code generator.
- Device logging with SignalLogger and data logging with Epilogue and DogLog.
- Device Fault Logging as telemetry for at-home testing. The faults are also logged with DogLog for post-match review.
- Pre-match self-check with custom self-check commands.
- A custom `VisionPoseEstimator` class that reads from a Photon Vision camera and updates the swerve pose estimator with filtered and disambiguated AprilTag vision measurements.
- Automated wheel radius characterization routine (based on 6328's wheel characterization).
- A custom zshell script that performs camera calibration with mrcal given an input video of a chessboard dance.
- Choreo support.# Todo
- Add more unit tests.
- Add proper licenses.# How This Will Be Used
- At the start of the 2025 season this project should be re-imported in the 2025 wpilib release for any additional updates.
- After that, since this project is a template on github, the actual 2025 robot code repo can be generated from this template:
# Calibration Process
- Run `calibration/calibrator.zsh` in zshell in WSL in the `calibration` folder (make sure all packages are installed).
- Analyze calibration as according to the mrcal tour.
- If the calibration was good, load the generated JSON into photon vision.