Merge pull request #236 from robotpy/more-examples

Add missing examples (via codex)

Author: virtuald

examples/robot/DifferentialDrivePoseEstimator/drivetrain.py

@@ -0,0 +1,260 @@
+#
+# Copyright (c) FIRST and other WPILib contributors.
+# Open Source Software; you can modify and/or share it under the terms of
+# the WPILib BSD license file in the root directory of this project.
+#
+
+import math
+
+import ntcore
+import wpilib
+import wpilib.simulation
+import wpimath
+import wpimath.units
+import robotpy_apriltag
+
+
+class Drivetrain:
+    """Represents a differential drive style drivetrain."""
+
+    kMaxSpeed = 3.0  # meters per second
+    kMaxAngularSpeed = math.tau  # one rotation per second
+
+    kTrackwidth = 0.381 * 2  # meters
+    kWheelRadius = 0.0508  # meters
+    kEncoderResolution = 4096
+
+    def __init__(self, cameraToObjectTopic: ntcore.DoubleArrayTopic) -> None:
+        self.leftLeader = wpilib.PWMSparkMax(1)
+        self.leftFollower = wpilib.PWMSparkMax(2)
+        self.rightLeader = wpilib.PWMSparkMax(3)
+        self.rightFollower = wpilib.PWMSparkMax(4)
+
+        self.leftEncoder = wpilib.Encoder(0, 1)
+        self.rightEncoder = wpilib.Encoder(2, 3)
+
+        self.imu = wpilib.OnboardIMU(wpilib.OnboardIMU.MountOrientation.kFlat)
+
+        self.leftPIDController = wpimath.PIDController(1, 0, 0)
+        self.rightPIDController = wpimath.PIDController(1, 0, 0)
+
+        self.kinematics = wpimath.DifferentialDriveKinematics(self.kTrackwidth)
+
+        self.robotToCamera = wpimath.Transform3d(
+            wpimath.Translation3d(1, 1, 1),
+            wpimath.Rotation3d(0, 0, math.pi / 2),
+        )
+
+        self.defaultVal = [0.0] * 7
+        self.cameraToObjectEntry = cameraToObjectTopic.getEntry(self.defaultVal)
+
+        layout = robotpy_apriltag.AprilTagFieldLayout.loadField(
+            robotpy_apriltag.AprilTagField.k2024Crescendo
+        )
+        self.objectInField = layout.getTagPose(0) or wpimath.Pose3d()
+
+        self.fieldSim = wpilib.Field2d()
+        self.fieldApproximation = wpilib.Field2d()
+
+        # Here we use DifferentialDrivePoseEstimator so that we can fuse odometry readings. The
+        # numbers used below are robot specific, and should be tuned.
+        self.poseEstimator = wpimath.DifferentialDrivePoseEstimator(
+            self.kinematics,
+            self.imu.getRotation2d(),
+            self.leftEncoder.getDistance(),
+            self.rightEncoder.getDistance(),
+            wpimath.Pose2d(),
+            (0.05, 0.05, wpimath.units.degreesToRadians(5)),
+            (0.5, 0.5, wpimath.units.degreesToRadians(30)),
+        )
+
+        # Gains are for example purposes only - must be determined for your own robot!
+        self.feedforward = wpimath.SimpleMotorFeedforwardMeters(1, 3)
+
+        # Simulation classes
+        self.leftEncoderSim = wpilib.simulation.EncoderSim(self.leftEncoder)
+        self.rightEncoderSim = wpilib.simulation.EncoderSim(self.rightEncoder)
+        self.drivetrainSystem = wpimath.LinearSystemId.identifyDrivetrainSystem(
+            1.98, 0.2, 1.5, 0.3
+        )
+        self.drivetrainSimulator = wpilib.simulation.DifferentialDrivetrainSim(
+            self.drivetrainSystem,
+            self.kTrackwidth,
+            wpimath.DCMotor.CIM(2),
+            8,
+            self.kWheelRadius,
+        )
+
+        self.imu.resetYaw()
+
+        self.leftLeader.addFollower(self.leftFollower)
+        self.rightLeader.addFollower(self.rightFollower)
+
+        # We need to invert one side of the drivetrain so that positive voltages
+        # result in both sides moving forward. Depending on how your robot's
+        # gearbox is constructed, you might have to invert the left side instead.
+        self.rightLeader.setInverted(True)
+
+        # Set the distance per pulse for the drive encoders. We can simply use the
+        # distance traveled for one rotation of the wheel divided by the encoder
+        # resolution.
+        self.leftEncoder.setDistancePerPulse(
+            math.tau * self.kWheelRadius / self.kEncoderResolution
+        )
+        self.rightEncoder.setDistancePerPulse(
+            math.tau * self.kWheelRadius / self.kEncoderResolution
+        )
+
+        self.leftEncoder.reset()
+        self.rightEncoder.reset()
+
+        wpilib.SmartDashboard.putData("Field", self.fieldSim)
+        wpilib.SmartDashboard.putData("FieldEstimation", self.fieldApproximation)
+
+    def setSpeeds(self, speeds: wpimath.DifferentialDriveWheelSpeeds) -> None:
+        """Sets the desired wheel speeds.
+
+        :param speeds: The desired wheel speeds.
+        """
+        leftFeedforward = self.feedforward.calculate(speeds.left)
+        rightFeedforward = self.feedforward.calculate(speeds.right)
+
+        leftOutput = self.leftPIDController.calculate(
+            self.leftEncoder.getRate(), speeds.left
+        )
+        rightOutput = self.rightPIDController.calculate(
+            self.rightEncoder.getRate(), speeds.right
+        )
+        self.leftLeader.setVoltage(leftOutput + leftFeedforward)
+        self.rightLeader.setVoltage(rightOutput + rightFeedforward)
+
+    def drive(self, xSpeed: float, rot: float) -> None:
+        """Drives the robot with the given linear velocity and angular velocity.
+
+        :param xSpeed: Linear velocity in m/s.
+        :param rot: Angular velocity in rad/s.
+        """
+        wheelSpeeds = self.kinematics.toWheelSpeeds(
+            wpimath.ChassisSpeeds(xSpeed, 0.0, rot)
+        )
+        self.setSpeeds(wheelSpeeds)
+
+    def publishCameraToObject(
+        self,
+        objectInField: wpimath.Pose3d,
+        robotToCamera: wpimath.Transform3d,
+        cameraToObjectEntry: ntcore.DoubleArrayEntry,
+        drivetrainSimulator: wpilib.simulation.DifferentialDrivetrainSim,
+    ) -> None:
+        """Computes and publishes to a network tables topic the transformation from the
+        camera's pose to the object's pose. This function exists solely for the
+        purposes of simulation, and this would normally be handled by computer vision.
+
+        The object could be a target or a fiducial marker.
+
+        :param objectInField: The object's field-relative position.
+        :param robotToCamera: The transformation from the robot's pose to the camera's pose.
+        :param cameraToObjectEntry: The networktables entry publishing and querying example
+            computer vision measurements.
+        """
+        robotInField = wpimath.Pose3d(drivetrainSimulator.getPose())
+        cameraInField = robotInField.transformBy(robotToCamera)
+        cameraToObject = wpimath.Transform3d(cameraInField, objectInField)
+
+        # Publishes double array with Translation3D elements {x, y, z} and Rotation3D elements
+        # {w, x, y, z} which describe the cameraToObject transformation.
+        quaternion = cameraToObject.rotation().getQuaternion()
+        val = [
+            cameraToObject.x,
+            cameraToObject.y,
+            cameraToObject.z,
+            quaternion.w,
+            quaternion.x,
+            quaternion.y,
+            quaternion.z,
+        ]
+        cameraToObjectEntry.set(val)
+
+    def objectToRobotPose(
+        self,
+        objectInField: wpimath.Pose3d,
+        robotToCamera: wpimath.Transform3d,
+        cameraToObjectEntry: ntcore.DoubleArrayEntry,
+    ) -> wpimath.Pose3d:
+        """Queries the camera-to-object transformation from networktables to compute the robot's
+        field-relative pose from vision measurements.
+
+        The object could be a target or a fiducial marker.
+
+        :param objectInField: The object's field-relative pose.
+        :param robotToCamera: The transformation from the robot's pose to the camera's pose.
+        :param cameraToObjectEntry: The networktables entry publishing and querying example
+            computer vision measurements.
+        """
+        val = cameraToObjectEntry.get()
+
+        # Reconstruct cameraToObject Transform3d from networktables.
+        translation = wpimath.Translation3d(val[0], val[1], val[2])
+        rotation = wpimath.Rotation3d(
+            wpimath.Quaternion(val[3], val[4], val[5], val[6])
+        )
+        cameraToObject = wpimath.Transform3d(translation, rotation)
+
+        cameraInField = objectInField.transformBy(cameraToObject.inverse())
+        robotInField = cameraInField.transformBy(robotToCamera.inverse())
+        return robotInField
+
+    def updateOdometry(self) -> None:
+        """Updates the field-relative position."""
+        self.poseEstimator.update(
+            self.imu.getRotation2d(),
+            self.leftEncoder.getDistance(),
+            self.rightEncoder.getDistance(),
+        )
+
+        # Publish cameraToObject transformation to networktables --this would normally be handled by
+        # the computer vision solution.
+        self.publishCameraToObject(
+            self.objectInField,
+            self.robotToCamera,
+            self.cameraToObjectEntry,
+            self.drivetrainSimulator,
+        )
+
+        # Compute the robot's field-relative position exclusively from vision measurements.
+        visionMeasurement3d = self.objectToRobotPose(
+            self.objectInField, self.robotToCamera, self.cameraToObjectEntry
+        )
+
+        # Convert robot pose from Pose3d to Pose2d needed to apply vision measurements.
+        visionMeasurement2d = visionMeasurement3d.toPose2d()
+
+        # Apply vision measurements. For simulation purposes only, we don't input a latency delay --
+        # on a real robot, this must be calculated based either on known latency or timestamps.
+        self.poseEstimator.addVisionMeasurement(
+            visionMeasurement2d,
+            wpilib.Timer.getTimestamp(),
+        )
+
+    def simulationPeriodic(self) -> None:
+        """This function is called periodically during simulation."""
+        # To update our simulation, we set motor voltage inputs, update the
+        # simulation, and write the simulated positions and velocities to our
+        # simulated encoder and gyro.
+        self.drivetrainSimulator.setInputs(
+            self.leftLeader.get() * wpilib.RobotController.getInputVoltage(),
+            self.rightLeader.get() * wpilib.RobotController.getInputVoltage(),
+        )
+        self.drivetrainSimulator.update(0.02)
+
+        self.leftEncoderSim.setDistance(self.drivetrainSimulator.getLeftPosition())
+        self.leftEncoderSim.setRate(self.drivetrainSimulator.getLeftVelocity())
+        self.rightEncoderSim.setDistance(self.drivetrainSimulator.getRightPosition())
+        self.rightEncoderSim.setRate(self.drivetrainSimulator.getRightVelocity())
+        # self.gyroSim.setAngle(-self.drivetrainSimulator.getHeading().getDegrees())
+
+    def periodic(self) -> None:
+        """This function is called periodically, no matter the mode."""
+        self.updateOdometry()
+        self.fieldSim.setRobotPose(self.drivetrainSimulator.getPose())
+        self.fieldApproximation.setRobotPose(self.poseEstimator.getEstimatedPosition())

examples/robot/DifferentialDrivePoseEstimator/exampleglobalmeasurementsensor.py

@@ -0,0 +1,34 @@
+#
+# Copyright (c) FIRST and other WPILib contributors.
+# Open Source Software; you can modify and/or share it under the terms of
+# the WPILib BSD license file in the root directory of this project.
+#
+
+import random
+
+import wpimath
+import wpimath.units
+
+
+class ExampleGlobalMeasurementSensor:
+    """This dummy class represents a global measurement sensor, such as a computer vision
+    solution.
+    """
+
+    def __init__(self) -> None:
+        raise RuntimeError("Utility class")
+
+    @staticmethod
+    def getEstimatedGlobalPose(estimatedRobotPose: wpimath.Pose2d) -> wpimath.Pose2d:
+        """Get a "noisy" fake global pose reading.
+
+        :param estimatedRobotPose: The robot pose.
+        """
+        rand_x = random.gauss(0.0, 0.5)
+        rand_y = random.gauss(0.0, 0.5)
+        rand_rot = random.gauss(0.0, wpimath.units.degreesToRadians(30))
+        return wpimath.Pose2d(
+            estimatedRobotPose.x + rand_x,
+            estimatedRobotPose.y + rand_y,
+            estimatedRobotPose.rotation().rotateBy(wpimath.Rotation2d(rand_rot)),
+        )

examples/robot/DifferentialDrivePoseEstimator/robot.py

@@ -0,0 +1,52 @@
+#!/usr/bin/env python3
+#
+# Copyright (c) FIRST and other WPILib contributors.
+# Open Source Software; you can modify and/or share it under the terms of
+# the WPILib BSD license file in the root directory of this project.
+#
+
+import ntcore
+import wpilib
+import wpimath
+
+from drivetrain import Drivetrain
+
+
+class MyRobot(wpilib.TimedRobot):
+    def __init__(self) -> None:
+        super().__init__()
+
+        self.inst = ntcore.NetworkTableInstance.getDefault()
+        self.doubleArrayTopic = self.inst.getDoubleArrayTopic("m_doubleArrayTopic")
+
+        self.controller = wpilib.XboxController(0)
+        self.drive = Drivetrain(self.doubleArrayTopic)
+
+        # Slew rate limiters to make joystick inputs more gentle; 1/3 sec from 0 to 1.
+        self.speedLimiter = wpimath.SlewRateLimiter(3)
+        self.rotLimiter = wpimath.SlewRateLimiter(3)
+
+    def autonomousPeriodic(self) -> None:
+        self.teleopPeriodic()
+        self.drive.updateOdometry()
+
+    def simulationPeriodic(self) -> None:
+        self.drive.simulationPeriodic()
+
+    def robotPeriodic(self) -> None:
+        self.drive.periodic()
+
+    def teleopPeriodic(self) -> None:
+        # Get the x speed. We are inverting this because Xbox controllers return
+        # negative values when we push forward.
+        xSpeed = -self.speedLimiter.calculate(self.controller.getLeftY())
+        xSpeed *= Drivetrain.kMaxSpeed
+
+        # Get the rate of angular rotation. We are inverting this because we want a
+        # positive value when we pull to the left (remember, CCW is positive in
+        # mathematics). Xbox controllers return positive values when you pull to
+        # the right by default.
+        rot = -self.rotLimiter.calculate(self.controller.getRightX())
+        rot *= Drivetrain.kMaxAngularSpeed
+
+        self.drive.drive(xSpeed, rot)