Code written by FIRST Robotics Competition Team 167, Children of the Corn for the 2024 FRC season.
AdvantageKit's "log literally everything" approach is used here, to allow for extremely robust debugging and simulations. In addition, it supports high frequency odometry and vision-based pose estimation, giving it high confidence in knowing where the robot is on the field at all times.
This project uses the GNU-GPL v3 open-source license. Due to the nature of the GNU-GPL v3 license, we cannot under any circumstances, provide warranty for the function of this code. For more information, read the LICENSE
file or read a summary of the license here.
- Always commit any changes before deploying code to the robot.
- When committing changes, always include the name of the person committing in the commit description. The commit description should also contain details of what actually changed in the commit.
- The Google Java Style Guide is enforced via the Spotless plugin for gradle. It is recommended to use the Spotless Gradle extension for VSCode as your Java formatter so that the code style guidelines are always followed.
- Programmers should use AdvantageScope for telemetry data, and drivers should use Elastic for match data.
- VSCode's highlighter is known to kinda... forget where classes are, and this happens most often with classes autogenerated by AdvantageKit. (We once had an issue where the highlighter said that
String
doesn't exist) To fix this, compile the code, reload VScode a bunch, and if those both fail, open the command palette and runJava: Clean Java Language Server Workspace
.
Contributors:
Code taken from the following places:
- AdvantageKit Advanced Swerve Example
- PathPlanner AdvantageKit-Compatible Pathfinding
- Sciborgs Spark Configuration Utilities
- See Git commit history for specifics on changes.