(From java package `lib5k.kinematics`)

# Drivebase Kinematics

“Kinematics is a branch of classical mechanics that describes the motion of points, bodies, and systems of bodies without considering the forces that cause them to move” -Wikipedia

Kinematics is very helpful for controlling, and defining robot movement during the autonomous period, along with various other in-game actions. This document will outline how we have integrated both forward, and inverse kinematics into Lib5K, and how to use these features in your robot program.

## Robot Localization

Robot localization, a form of forward kinematics, is the process of locating a robot in 2D or 3D space using a variety of sensors. Think of it like a GPS.

### Localization with an FRC robot

Assuming a kitbot-style drivetrain, we can use three simple sensors, and some trigonometry to determine the robot’s location on the field with reasonable precision.

We need to know three things about the robot.

• Total distance traveled
• Circumference of the wheels in meters

With this information, we can get an X, and Y coordinate of the robot in meters with the following code:

``````
// For storing the robot position
double x,y = 0.0;
double lastPos = 0.0;

// Calculate the circumference in meters of the wheels
// This assumes 6.0 inch wheels on the drivebase
final double wheelCirc = (6.0 * 0.0254) * Math.PI;

// Number of encoder ticks produced per wheel revolution
final int ticksPerRev = 720;

/**
* Will convert an encoder tick count to a distance traveled in meters
*/
double ticksToMeters(int tickCount){
return (tickCount / ticksPerRev) * wheelCirc;
}

/**
* This should be called once per 20ms (or the robot periodic loop)
*/
void loop(){
// Read the robot's current angle from the gyroscope and bind it by 360 degrees
double heading = getAngle() % 360;

// Find the average distance traveled between each side of the robot. This
// will give the total Y distance traveled by the robot, accounting for rotation
double leftMeters = ticksToMeters(getLeftEncoderTicks());
double rightMeters = ticksToMeters(getRightEncoderTicks());

double position = (leftMeters + rightMeters) / 2.0;

// Find the distance traveled by the robot since the last time loop() was called
double distance = position - lastPos;

// Calculate position

// re-set the last position
lastPos = position;
}

``````

This example assumes that the robot’s heading / angle can be read with `getAngle()`, and each encoder has a method to read its tick count. If using the tools built in to Lib5K, all of this is handled for you.

#### How it works

Finding the robot location is relatively simple with the help of some trigonometry. This diagram roughly demonstrates how location is calculated. In practice, calling the `loop()` method in a loop would generate a very large number of triangles, and provide much greater precision.

The diagram shows the calculation that would be completed if the robot started on HAB1 (at the red angle), and moved to the left rocket, then called `loop()` once. The robot’s heading would be treated as theta (the red angle), and it’s total distance traveled since the last loop as the hypotenuse. The labeled `X` and `Y` axis of the right-angled triangle are now the robot’s `X,Y` location on the field.

### Lib5K LocalizationEngine

Lib5K provides a tool called the `LocalizationEngine`. This tool wil automatically complete the localization calculations, and convert the data to a `FieldPosition` object for easy use with other Lib5K tools and components, like the `MovementPlanner`.

#### Using it in your code

The `LocalizationEngine` requires some other components to work. The below example will assume you are using a robot similar to MiniBot.

To use the `LocalizationEngine`, the same three sensors from the example above are required. Here, we will define our motors, their encoders, and a Gyroscope object. This code should be part of the DriveTrain subsystem.

``````// Create two gearboxes with encoders attached to the rear motor controllers
GearBox leftGearbox = new GearBox(new WPI_TalonSRX(1), new WPI_TalonSRX(2), true);
GearBox rightGearbox = new GearBox(new WPI_TalonSRX31), new WPI_TalonSRX(4), true);

// Create two encoders from each gearbox
EncoderBase leftEncoder = new GeaBoxEncoder(leftGearbox);
EncoderBase rightEncoder = new GeaBoxEncoder(rightGearbox);

// Create a gyroscope (this example wil use a NavX)
AHRS gyro = new AHRS(Port.kMXP);
``````

We additionally will need to get a `LocalizationEngine` instance to work with

``````// Get the current LocalizationEngine instance
LocalizationEngine le_instance = LocalizationEngine.getInstance();
``````

The codebase should have these values defined elsewhere (e.g. in the `Constants.java` file), but for this example, we will define a few parameters of the robot here:

``````// Calculate the circumference in meters of the wheels
// This assumes 6.0 inch wheels on the drivebase
final double wheelCirc = (6.0 * 0.0254) * Math.PI;

// Number of encoder ticks produced per wheel revolution
final int ticksPerRev = 720;
``````

Next, in the subsystem’s `periodic()` method, or any other constantly looping method, we can update the `LocalizationEngine`

``````@Override
public void periodic(){

// Get each encoder's distance reading
double leftMeters = leftEncoder.getMeters(ticksPerRev, wheelCirc);
double rightMeters = rightEncoder.getMeters(ticksPerRev, wheelCirc);

// Update the LocalizationEngine
}
``````

The robot’s position will now be constantly updated. At the start of autonomous, you may want to reset the robot’s location.

``````// Set the robot's field-relitive location in meters.
// This example uses an X of 0, and a Y of 5 meters.
// This should be called at the start of each autonomous
// commandgroup to set the location appropriately.
le_instance.setRobotPosition(new FieldPosition(0.0, 5.0));
``````

Finally, to read the robot’s current location (and heading), the following can be done:

``````FieldPosition currentPosition = le_instance.getRobotPosition();
``````

## Path following

As long as there is a reliable way to determine the robot’s location on the field, we can build a simple, real-time pathing system. This form of inverse kinematics is fairly common among autonomous path-following systems built by top tier FRC teams like 254, and 1114.

### Point-to-Point movement

We know where we are, and can easily define where we want to be with the help of a `FieldPosition`. With these two points, and some some basic control theory, we can instruct the robot to move smoothly between the two. Effectively forming a path.

To do this, we ___.