Autonomous mode is easier and more effective with the RoadRunner toolkit. It's not easy to understand all at once, but it makes a lot of sense if we look a little bit at a time.
I believe RoadRunner was developed by Team #8367, Acme Robotics, around 2018 to address the need for more precise and efficient motion planning in FTC competitions. Before RoadRunner, many teams relied on less advanced methods for robot trajectory control, limiting the precision of their autonomous routines. By 2019 and 2020, RoadRunner gained widespread adoption. It has since become a go-to tool for FTC teams seeking smooth, accurate robot movement during autonomous periods.
RoadRunner is a motion planning library designed for FTC robots, allowing teams to create precise, smooth trajectories for autonomous movement. It uses kinematic models, such as mecanum and tank drive, to account for a robot’s unique drivetrain and behavior. RoadRunner operates by defining Pose2d (position and orientation) coordinates and allows teams to create paths with splines (curved paths) and waypoints (specific positions the robot should pass through). The library incorporates feedforward control and supports tuning for optimal motion via parameters like kV, kA, and track width, providing fine-tuned trajectory generation for complex autonomous tasks.
VOCABULARY
Pose2d: Defines the robot’s position (x, y) and orientation (heading) on the field.
Spline: A smooth, curved path that the robot follows during its trajectory.
Waypoint: Specific points that the robot must pass through on its path.
Feedforward Control: A method of controlling robot movement based on predicted motion, helping smooth trajectories.
kV: A constant used to model the robot’s velocity.
kA: A constant used to model the robot’s acceleration.
Track Width: The distance between the left and right wheels of a robot, important for accurately modeling turns.
Mecanum Drive: A type of drivetrain that allows for omnidirectional movement using angled wheels.
Tank Drive: A drivetrain setup where each side of the robot operates independently, allowing for tight turns.
Odometry: Use of data from motion sensors to estimate change in position over time. It is used in robotics by some legged or wheeled robots to estimate their position relative to a starting location. This method is sensitive to errors due to the integration of velocity measurements over time to give position estimates
To integrate FTCLib with RoadRunner, we start by modifying the provided Mecanum Drive class. First, we remove its status as final, allowing us to extend it. We then make this class extend FTCLib’s Subsystem class, renaming it to RoadRunner for clarity. We inject our parameters using the Params object but keep the rest of the logic minimal in this class. Instead, we place our custom functionality in the Mecanum Drive class, where we define any additional logic or custom functions tailored to our specific robot. This separation allows RoadRunner to handle trajectory generation while our custom drive logic remains organized in Mecanum Drive.