This PR changes the spline parameterizer to use an explicit stack instead of recursion. This is motivated by the fact that splines with adjacent waypoints with approximately opposite headings will never parameterize. In this case the parameterizer subdivides these malformed splines fine for a while, and then gets stuck parameterizing infinitely on some interval. In the recursive approach, this would lead to a stack overflow. We could implement a recursion depth counter (this is what my team did on our similar trajectory code last season), but it's hard to choose a good number for max depth because the initial amount of stack used varies based on how the user calls Parameterize.
A good solution for this is converting the recursion to an "explicit stack," which basically simulates recursion, but allows us to have a much larger maximum stack size. Because we avoid the stack overflow, we can instead throws a more informative MalformedSplineException. If the user is using the TrajectoryGenerator instead of the SplineParameterizer directly then the TrajectoryGenerator will go ahead and catch the exception, return a harmless empty trajectory, and report and error to the driver station.
Both floating point and 8-bit integer classes are included, as well as a wide selection of color constants.
Co-authored-by: Austin Shalit <austinshalit@gmail.com>
This is extremely useful for implementing various "ramping" functions
(such as voltage ramps, setpoint ramps, etc). Usage is straightforward;
it behaves like all of our other filter classes. C++ version is unit-safe.
- new CommandScheduler
- kinematics and odometry classes
- new PIDController
- ProfiledPIDController
- TrapezoidProfile (reported in Constraints class)
Also update instances.txt to match latest NI version.
One side effect is that a couple of classes are no longer constexpr.
It doesn't make sense to continue to provide a less accurate method of performing odometry
when a more accurate method using distances exists.
This also removes the need to pass DifferentialDriveKinematics to the constructor.
This kind of filter is extremely useful for signals that are susceptible to sudden
outliers - ultrasonics, 1-D LIDAR, and results from vision processing are all
good use-cases.
This also modifies the existing ultrasonic examples accordingly.
The odometry classes previously took in the robot angle as an argument, meaning that users had to take care of offsetting the gyro themselves to accurately report the robot angle. This change will make it so that users will not have to worry about resetting gyros and adding offsets themselves, as this will be handled by the odometry classes.
