allwpilib VS autodiff

You need to be more specific, what issues? I would recommend starting with an example. https://github.com/wpilibsuite/allwpilib/tree/main/wpilibjExamples/src/main/java/edu/wpi/first/wpilibj/examples/mecanumdrive. And looking at the documentation for it here. https://docs.wpilib.org/en/stable/docs/software/hardware-apis/motors/wpi-drive-classes.html#mecanum-drive If this doesn’t work make sure your wheels are in the correct orientation (x pattern when viewed from top) and you set your inverts right.)

I believe this page has timed based swerve code https://github.com/wpilibsuite/allwpilib/tree/main/wpilibjExamples/src/main/java/edu/wpi/first/wpilibj/examples/swervebot

Study Java syntax, and look up the common code libraries at https://wpilib.org/

Just to cover all the bases, have you run the 2022 installer? That should have all of the files required to build robot projects. Even if you have I would recommend running it again and making sure that it installs the JDK to the default location.

Also remember that wpilib is open source, so you can check out the implantation of all of these classes yourself to better understand what they’re doing. Java SequentialCommandGroup is right here. You can also access this by control+left clicking on almost any class or method in vscode and it’ll open up a new editor tab with that code.

To get myself started I used Manning Robotics tutorial. Keep in mind though it is out of date as there was a rewrite for the command based framework in 2020. Check out the WPILIB for a walk through of the system. Also linked from WPILIB are the example projects gears bot and traditional hatchbot that my team has been referencing to get us started.

It's been updated, and installing it worked for me. Get the dmg from here, then download and install.

This is the link to the GitHub release https://github.com/wpilibsuite/allwpilib/releases/tag/v2019.4.1

if you’re sure you want to go the in-wheel encoder route, check out wpilib’s implementation of it here. it uses EJML for most of the math, but you can remove it if you’re comfortable with matrixes

I completely agree. Specifying the metric rather than the Christoffel symbols would make it much easier for the users. Something like https://github.com/autodiff/autodiff might just work as the metric tensor is made up of primitives.

Old username :D So far I have been using Eigen for linear algebra and NLOPT for optimization algorithms. I have found "autodiff" that hopefully looks easy to use: https://github.com/autodiff/autodiff

Forward-mode differentiation is easy to implement in C++ with templates, operator overloading, and dual numbers (https://en.wikipedia.org/wiki/Automatic_differentiation#Auto...). Some libraries such as autodiff (https://github.com/autodiff/autodiff) and CppAD (https://github.com/coin-or/CppAD) use this method.

I was thinking of adding [autodiff](https://github.com/autodiff/autodiff) in the future, mainly because it works seamlessly with *Eigen*. One big advantage would be that I could use it for AD for NonlinearSystems as well.