A bit of an update on the software side of the house.
We have more students than ever on our programming team and we’re working on two things in parallel at the moment.
First is that we have a test chassis with Mk4 SDS modules on it and we are using it to train and learn about the Phoenix 6 upgrades to the CTRE library as we plan to continue using TalonFX powered motors (Falcon/Kraken) this season.
We setup the drivetrain using the TunerX application and generated a template project. We set off to work creating a custom command class that performs a set of behavior similar to our code base last season for applying speed limiting on the drivetrain as well as a set of cardinal direction rotation controls. We had to familiarize ourselves with the new SwerveRequest objects that CTRE introduced to their Phoenix 6 Swerve library.
Once that task was completed we spent some time trying out some of the different dashboard options available to us to display information about our drivetrain to start. We really like using AdvantageScope’s Swerve widget and other charting and graphing tools and we believe that we will be making heavy use of it during our development/testing this build season.
We also really like the Shuffleboard alternative that was released here on CD called Elastic.
We will be testing it out as a candidate for our competition dashboard.
Lastly on the drivetrain side we finally got to work trialing the changes made to PathPlanner 2024. We manually implemented a path following command into our swerve drivetrain code base last year, but we wanted to start fresh with our new students reading the documentation and implementing a solution that they liked. This lead us to trying out the AutoBuilder method and our first trial runs were extremely straightforward and easy to setup. We built a multi-path auto program in no time at all and are now working on setting up our practice field area to mock up the real field and note positions to start creating competition viable autonomous paths.
Next we began testing out our Limelight3’s with the latest revision of the AprilTags. We don’t have a ton of information to share yet, but getting the detection working in a very basic sense was super simple. We did some basic comparisons to the PhotonVision image for the Limelight3, but it’s too early for us to say which software package we will be running. The community is full of very helpful folks and we already have more information to look into at our next meeting to optimize the performance of our cameras.
Lastly we managed to trial the object detection using a Google Coral on our Limelight3 and we got some pretty cool results. We used the model linked below and managed to get our Limelight detecting notes in nearly every orientation really well. The framerate was on the low end, but we have yet to discover how high of a framerate we need for our neutral zone auton plans. If we need to go higher framerate we are considering an OrangePi 5 + PhotonVision alternative to potentially get upwards of 30+ FPS.