Reorganize directory structure for third robot.
I added a "bot3" directory, so that we can have all the third
robot specific stuff live there.
Most of these changes were just copying files from frc971. Many
of the files have no modifications except changed paths in the
include directives, changed paths in gyp files, and changed
namespace names. Some of the code is new, however, for instance,
I modified motor_writer to control the motors and pistons on
the third robot, as well as added a new "rollers" queue to
transfer data pertaining to these.
I also updated the build system so that builds for the normal
robot code and the third robot code would in no way conflict
with each other. Finally, I figured out how I want to handle
the constants, although I haven't put actual values for
that in yet.
I cleaned up the python code, adding in the small changes to
that which Comran wanted.
Change-Id: I04e17dc8b17a980338b718a78e348ea647ec060b
diff --git a/bot3/control_loops/python/drivetrain.py b/bot3/control_loops/python/drivetrain.py
new file mode 100755
index 0000000..65faf2a
--- /dev/null
+++ b/bot3/control_loops/python/drivetrain.py
@@ -0,0 +1,239 @@
+#!/usr/bin/python
+
+import control_loop
+import controls
+import numpy
+import sys
+from matplotlib import pylab
+
+
+class CIM(control_loop.ControlLoop):
+ def __init__(self):
+ super(CIM, self).__init__("CIM")
+ # Stall Torque in N m
+ self.stall_torque = 2.42
+ # Stall Current in Amps
+ self.stall_current = 133
+ # Free Speed in RPM
+ self.free_speed = 4650.0
+ # Free Current in Amps
+ self.free_current = 2.7
+ # Moment of inertia of the CIM in kg m^2
+ self.J = 0.0001
+ # Resistance of the motor, divided by 2 to account for the 2 motors
+ self.R = 12.0 / self.stall_current
+ # Motor velocity constant
+ self.Kv = ((self.free_speed / 60.0 * 2.0 * numpy.pi) /
+ (12.0 - self.R * self.free_current))
+ # Torque constant
+ self.Kt = self.stall_torque / self.stall_current
+ # Control loop time step
+ self.dt = 0.01
+
+ # State feedback matrices
+ self.A_continuous = numpy.matrix(
+ [[-self.Kt / self.Kv / (self.J * self.R)]])
+ self.B_continuous = numpy.matrix(
+ [[self.Kt / (self.J * self.R)]])
+ self.C = numpy.matrix([[1]])
+ self.D = numpy.matrix([[0]])
+
+ self.A, self.B = self.ContinuousToDiscrete(self.A_continuous,
+ self.B_continuous, self.dt)
+
+ self.PlaceControllerPoles([0.01])
+ self.PlaceObserverPoles([0.01])
+
+ self.U_max = numpy.matrix([[12.0]])
+ self.U_min = numpy.matrix([[-12.0]])
+
+ self.InitializeState()
+
+
+class Drivetrain(control_loop.ControlLoop):
+ def __init__(self, name="Drivetrain", left_low=True, right_low=True):
+ super(Drivetrain, self).__init__(name)
+ # Stall Torque in N m
+ self.stall_torque = 2.42
+ # Stall Current in Amps
+ self.stall_current = 133
+ # Free Speed in RPM. Used number from last year.
+ self.free_speed = 4650.0
+ # Free Current in Amps
+ self.free_current = 2.7
+ # Moment of inertia of the drivetrain in kg m^2
+ # Just borrowed from last year.
+ self.J = 4.5
+ # Mass of the robot, in kg.
+ self.m = 60
+ # Radius of the robot, in meters (from last year).
+ self.rb = 0.617998644 / 2.0
+ # Radius of the wheels, in meters.
+ self.r = .04445
+ # Resistance of the motor, divided by the number of motors.
+ self.R = 12.0 / self.stall_current / 4
+ # Motor velocity constant
+ self.Kv = ((self.free_speed / 60.0 * 2.0 * numpy.pi) /
+ (12.0 - self.R * self.free_current))
+ # Torque constant
+ self.Kt = self.stall_torque / self.stall_current
+ # Gear ratios
+ self.G_low = 14.0 / 60.0 * 17.0 / 50.0
+ self.G_high = 30.0 / 44.0 * 17.0 / 50.0
+ if left_low:
+ self.Gl = self.G_low
+ else:
+ self.Gl = self.G_high
+ if right_low:
+ self.Gr = self.G_low
+ else:
+ self.Gr = self.G_high
+ # Control loop time step
+ self.dt = 0.01
+
+ # These describe the way that a given side of a robot will be influenced
+ # by the other side. Units of 1 / kg.
+ self.msp = 1.0 / self.m + self.rb * self.rb / self.J
+ self.msn = 1.0 / self.m - self.rb * self.rb / self.J
+ # The calculations which we will need for A and B.
+ self.tcl = -self.Kt / self.Kv / (self.Gl * self.Gl * self.R * self.r * self.r)
+ self.tcr = -self.Kt / self.Kv / (self.Gr * self.Gr * self.R * self.r * self.r)
+ self.mpl = self.Kt / (self.Gl * self.R * self.r)
+ self.mpr = self.Kt / (self.Gr * self.R * self.r)
+
+ # State feedback matrices
+ # X will be of the format
+ # [[positionl], [velocityl], [positionr], velocityr]]
+ self.A_continuous = numpy.matrix(
+ [[0, 1, 0, 0],
+ [0, self.msp * self.tcl, 0, self.msn * self.tcr],
+ [0, 0, 0, 1],
+ [0, self.msn * self.tcl, 0, self.msp * self.tcr]])
+ self.B_continuous = numpy.matrix(
+ [[0, 0],
+ [self.msp * self.mpl, self.msn * self.mpr],
+ [0, 0],
+ [self.msn * self.mpl, self.msp * self.mpr]])
+ self.C = numpy.matrix([[1, 0, 0, 0],
+ [0, 0, 1, 0]])
+ self.D = numpy.matrix([[0, 0],
+ [0, 0]])
+
+ #print "THE NUMBER I WANT" + str(numpy.linalg.inv(self.A_continuous) * -self.B_continuous * numpy.matrix([[12.0], [12.0]]))
+ self.A, self.B = self.ContinuousToDiscrete(
+ self.A_continuous, self.B_continuous, self.dt)
+
+ # Poles from last year.
+ self.hp = 0.65
+ self.lp = 0.83
+ self.PlaceControllerPoles([self.hp, self.lp, self.hp, self.lp])
+ print self.K
+ q_pos = 0.07
+ q_vel = 1.0
+ self.Q = numpy.matrix([[(1.0 / (q_pos ** 2.0)), 0.0, 0.0, 0.0],
+ [0.0, (1.0 / (q_vel ** 2.0)), 0.0, 0.0],
+ [0.0, 0.0, (1.0 / (q_pos ** 2.0)), 0.0],
+ [0.0, 0.0, 0.0, (1.0 / (q_vel ** 2.0))]])
+
+ self.R = numpy.matrix([[(1.0 / (12.0 ** 2.0)), 0.0],
+ [0.0, (1.0 / (12.0 ** 2.0))]])
+ self.K = controls.dlqr(self.A, self.B, self.Q, self.R)
+ print self.A
+ print self.B
+ print self.K
+ print numpy.linalg.eig(self.A - self.B * self.K)[0]
+
+ self.hlp = 0.3
+ self.llp = 0.4
+ self.PlaceObserverPoles([self.hlp, self.hlp, self.llp, self.llp])
+
+ self.U_max = numpy.matrix([[12.0], [12.0]])
+ self.U_min = numpy.matrix([[-12.0], [-12.0]])
+ self.InitializeState()
+
+def main(argv):
+ # Simulate the response of the system to a step input.
+ drivetrain = Drivetrain()
+ simulated_left = []
+ simulated_right = []
+ for _ in xrange(100):
+ drivetrain.Update(numpy.matrix([[12.0], [12.0]]))
+ simulated_left.append(drivetrain.X[0, 0])
+ simulated_right.append(drivetrain.X[2, 0])
+
+ #pylab.plot(range(100), simulated_left)
+ #pylab.plot(range(100), simulated_right)
+ #pylab.show()
+
+ # Simulate forwards motion.
+ drivetrain = Drivetrain()
+ close_loop_left = []
+ close_loop_right = []
+ R = numpy.matrix([[1.0], [0.0], [1.0], [0.0]])
+ for _ in xrange(100):
+ U = numpy.clip(drivetrain.K * (R - drivetrain.X_hat),
+ drivetrain.U_min, drivetrain.U_max)
+ drivetrain.UpdateObserver(U)
+ drivetrain.Update(U)
+ close_loop_left.append(drivetrain.X[0, 0])
+ close_loop_right.append(drivetrain.X[2, 0])
+
+ #pylab.plot(range(100), close_loop_left)
+ #pylab.plot(range(100), close_loop_right)
+ #pylab.show()
+
+ # Try turning in place
+ drivetrain = Drivetrain()
+ close_loop_left = []
+ close_loop_right = []
+ R = numpy.matrix([[-1.0], [0.0], [1.0], [0.0]])
+ for _ in xrange(100):
+ U = numpy.clip(drivetrain.K * (R - drivetrain.X_hat),
+ drivetrain.U_min, drivetrain.U_max)
+ drivetrain.UpdateObserver(U)
+ drivetrain.Update(U)
+ close_loop_left.append(drivetrain.X[0, 0])
+ close_loop_right.append(drivetrain.X[2, 0])
+
+ #pylab.plot(range(100), close_loop_left)
+ #pylab.plot(range(100), close_loop_right)
+ #pylab.show()
+
+ # Try turning just one side.
+ drivetrain = Drivetrain()
+ close_loop_left = []
+ close_loop_right = []
+ R = numpy.matrix([[0.0], [0.0], [1.0], [0.0]])
+ for _ in xrange(100):
+ U = numpy.clip(drivetrain.K * (R - drivetrain.X_hat),
+ drivetrain.U_min, drivetrain.U_max)
+ drivetrain.UpdateObserver(U)
+ drivetrain.Update(U)
+ close_loop_left.append(drivetrain.X[0, 0])
+ close_loop_right.append(drivetrain.X[2, 0])
+
+ #pylab.plot(range(100), close_loop_left)
+ #pylab.plot(range(100), close_loop_right)
+ #pylab.show()
+
+ # Write the generated constants out to a file.
+ print "Output one"
+ drivetrain_low_low = Drivetrain(name="DrivetrainLowLow", left_low=True, right_low=True)
+ drivetrain_low_high = Drivetrain(name="DrivetrainLowHigh", left_low=True, right_low=False)
+ drivetrain_high_low = Drivetrain(name="DrivetrainHighLow", left_low=False, right_low=True)
+ drivetrain_high_high = Drivetrain(name="DrivetrainHighHigh", left_low=False, right_low=False)
+
+ if len(argv) != 3:
+ print "Expected .h file name and .cc file name"
+ else:
+ dog_loop_writer = control_loop.ControlLoopWriter(
+ "Drivetrain", [drivetrain_low_low, drivetrain_low_high,
+ drivetrain_high_low, drivetrain_high_high],
+ namespaces = ["bot3", "control_loops"])
+ if argv[1][-3:] == '.cc':
+ dog_loop_writer.Write(argv[2], argv[1])
+ else:
+ dog_loop_writer.Write(argv[1], argv[2])
+
+if __name__ == '__main__':
+ sys.exit(main(sys.argv))
diff --git a/bot3/control_loops/python/polydrivetrain.py b/bot3/control_loops/python/polydrivetrain.py
new file mode 100755
index 0000000..49d390b
--- /dev/null
+++ b/bot3/control_loops/python/polydrivetrain.py
@@ -0,0 +1,501 @@
+#!/usr/bin/python
+
+import numpy
+import sys
+import polytope
+import drivetrain
+import control_loop
+import controls
+from matplotlib import pylab
+
+__author__ = 'Austin Schuh (austin.linux@gmail.com)'
+
+
+def CoerceGoal(region, K, w, R):
+ """Intersects a line with a region, and finds the closest point to R.
+
+ Finds a point that is closest to R inside the region, and on the line
+ defined by K X = w. If it is not possible to find a point on the line,
+ finds a point that is inside the region and closest to the line. This
+ function assumes that
+
+ Args:
+ region: HPolytope, the valid goal region.
+ K: numpy.matrix (2 x 1), the matrix for the equation [K1, K2] [x1; x2] = w
+ w: float, the offset in the equation above.
+ R: numpy.matrix (2 x 1), the point to be closest to.
+
+ Returns:
+ numpy.matrix (2 x 1), the point.
+ """
+ return DoCoerceGoal(region, K, w, R)[0]
+
+def DoCoerceGoal(region, K, w, R):
+ if region.IsInside(R):
+ return (R, True)
+
+ perpendicular_vector = K.T / numpy.linalg.norm(K)
+ parallel_vector = numpy.matrix([[perpendicular_vector[1, 0]],
+ [-perpendicular_vector[0, 0]]])
+
+ # We want to impose the constraint K * X = w on the polytope H * X <= k.
+ # We do this by breaking X up into parallel and perpendicular components to
+ # the half plane. This gives us the following equation.
+ #
+ # parallel * (parallel.T \dot X) + perpendicular * (perpendicular \dot X)) = X
+ #
+ # Then, substitute this into the polytope.
+ #
+ # H * (parallel * (parallel.T \dot X) + perpendicular * (perpendicular \dot X)) <= k
+ #
+ # Substitute K * X = w
+ #
+ # H * parallel * (parallel.T \dot X) + H * perpendicular * w <= k
+ #
+ # Move all the knowns to the right side.
+ #
+ # H * parallel * ([parallel1 parallel2] * X) <= k - H * perpendicular * w
+ #
+ # Let t = parallel.T \dot X, the component parallel to the surface.
+ #
+ # H * parallel * t <= k - H * perpendicular * w
+ #
+ # This is a polytope which we can solve, and use to figure out the range of X
+ # that we care about!
+
+ t_poly = polytope.HPolytope(
+ region.H * parallel_vector,
+ region.k - region.H * perpendicular_vector * w)
+
+ vertices = t_poly.Vertices()
+
+ if vertices.shape[0]:
+ # The region exists!
+ # Find the closest vertex
+ min_distance = numpy.infty
+ closest_point = None
+ for vertex in vertices:
+ point = parallel_vector * vertex + perpendicular_vector * w
+ length = numpy.linalg.norm(R - point)
+ if length < min_distance:
+ min_distance = length
+ closest_point = point
+
+ return (closest_point, True)
+ else:
+ # Find the vertex of the space that is closest to the line.
+ region_vertices = region.Vertices()
+ min_distance = numpy.infty
+ closest_point = None
+ for vertex in region_vertices:
+ point = vertex.T
+ length = numpy.abs((perpendicular_vector.T * point)[0, 0])
+ if length < min_distance:
+ min_distance = length
+ closest_point = point
+
+ return (closest_point, False)
+
+
+class VelocityDrivetrainModel(control_loop.ControlLoop):
+ def __init__(self, left_low=True, right_low=True, name="VelocityDrivetrainModel"):
+ super(VelocityDrivetrainModel, self).__init__(name)
+ self._drivetrain = drivetrain.Drivetrain(left_low=left_low,
+ right_low=right_low)
+ self.dt = 0.01
+ self.A_continuous = numpy.matrix(
+ [[self._drivetrain.A_continuous[1, 1], self._drivetrain.A_continuous[1, 3]],
+ [self._drivetrain.A_continuous[3, 1], self._drivetrain.A_continuous[3, 3]]])
+
+ self.B_continuous = numpy.matrix(
+ [[self._drivetrain.B_continuous[1, 0], self._drivetrain.B_continuous[1, 1]],
+ [self._drivetrain.B_continuous[3, 0], self._drivetrain.B_continuous[3, 1]]])
+ self.C = numpy.matrix(numpy.eye(2));
+ self.D = numpy.matrix(numpy.zeros((2, 2)));
+
+ self.A, self.B = self.ContinuousToDiscrete(self.A_continuous,
+ self.B_continuous, self.dt)
+
+ # FF * X = U (steady state)
+ self.FF = self.B.I * (numpy.eye(2) - self.A)
+
+ self.PlaceControllerPoles([0.6, 0.6])
+ self.PlaceObserverPoles([0.02, 0.02])
+
+ self.G_high = self._drivetrain.G_high
+ self.G_low = self._drivetrain.G_low
+ self.R = self._drivetrain.R
+ self.r = self._drivetrain.r
+ self.Kv = self._drivetrain.Kv
+ self.Kt = self._drivetrain.Kt
+
+ self.U_max = self._drivetrain.U_max
+ self.U_min = self._drivetrain.U_min
+
+
+class VelocityDrivetrain(object):
+ HIGH = 'high'
+ LOW = 'low'
+ SHIFTING_UP = 'up'
+ SHIFTING_DOWN = 'down'
+
+ def __init__(self):
+ self.drivetrain_low_low = VelocityDrivetrainModel(
+ left_low=True, right_low=True, name='VelocityDrivetrainLowLow')
+ self.drivetrain_low_high = VelocityDrivetrainModel(left_low=True, right_low=False, name='VelocityDrivetrainLowHigh')
+ self.drivetrain_high_low = VelocityDrivetrainModel(left_low=False, right_low=True, name = 'VelocityDrivetrainHighLow')
+ self.drivetrain_high_high = VelocityDrivetrainModel(left_low=False, right_low=False, name = 'VelocityDrivetrainHighHigh')
+
+ # X is [lvel, rvel]
+ self.X = numpy.matrix(
+ [[0.0],
+ [0.0]])
+
+ self.U_poly = polytope.HPolytope(
+ numpy.matrix([[1, 0],
+ [-1, 0],
+ [0, 1],
+ [0, -1]]),
+ numpy.matrix([[12],
+ [12],
+ [12],
+ [12]]))
+
+ self.U_max = numpy.matrix(
+ [[12.0],
+ [12.0]])
+ self.U_min = numpy.matrix(
+ [[-12.0000000000],
+ [-12.0000000000]])
+
+ self.dt = 0.01
+
+ self.R = numpy.matrix(
+ [[0.0],
+ [0.0]])
+
+ # ttrust is the comprimise between having full throttle negative inertia,
+ # and having no throttle negative inertia. A value of 0 is full throttle
+ # inertia. A value of 1 is no throttle negative inertia.
+ self.ttrust = 1.0
+
+ self.left_gear = VelocityDrivetrain.LOW
+ self.right_gear = VelocityDrivetrain.LOW
+ self.left_shifter_position = 0.0
+ self.right_shifter_position = 0.0
+ self.left_cim = drivetrain.CIM()
+ self.right_cim = drivetrain.CIM()
+
+ def IsInGear(self, gear):
+ return gear is VelocityDrivetrain.HIGH or gear is VelocityDrivetrain.LOW
+
+ def MotorRPM(self, shifter_position, velocity):
+ if shifter_position > 0.5:
+ return (velocity / self.CurrentDrivetrain().G_high /
+ self.CurrentDrivetrain().r)
+ else:
+ return (velocity / self.CurrentDrivetrain().G_low /
+ self.CurrentDrivetrain().r)
+
+ def CurrentDrivetrain(self):
+ if self.left_shifter_position > 0.5:
+ if self.right_shifter_position > 0.5:
+ return self.drivetrain_high_high
+ else:
+ return self.drivetrain_high_low
+ else:
+ if self.right_shifter_position > 0.5:
+ return self.drivetrain_low_high
+ else:
+ return self.drivetrain_low_low
+
+ def SimShifter(self, gear, shifter_position):
+ if gear is VelocityDrivetrain.HIGH or gear is VelocityDrivetrain.SHIFTING_UP:
+ shifter_position = min(shifter_position + 0.5, 1.0)
+ else:
+ shifter_position = max(shifter_position - 0.5, 0.0)
+
+ if shifter_position == 1.0:
+ gear = VelocityDrivetrain.HIGH
+ elif shifter_position == 0.0:
+ gear = VelocityDrivetrain.LOW
+
+ return gear, shifter_position
+
+ def ComputeGear(self, wheel_velocity, should_print=False, current_gear=False, gear_name=None):
+ high_omega = (wheel_velocity / self.CurrentDrivetrain().G_high /
+ self.CurrentDrivetrain().r)
+ low_omega = (wheel_velocity / self.CurrentDrivetrain().G_low /
+ self.CurrentDrivetrain().r)
+ #print gear_name, "Motor Energy Difference.", 0.5 * 0.000140032647 * (low_omega * low_omega - high_omega * high_omega), "joules"
+ high_torque = ((12.0 - high_omega / self.CurrentDrivetrain().Kv) *
+ self.CurrentDrivetrain().Kt / self.CurrentDrivetrain().R)
+ low_torque = ((12.0 - low_omega / self.CurrentDrivetrain().Kv) *
+ self.CurrentDrivetrain().Kt / self.CurrentDrivetrain().R)
+ high_power = high_torque * high_omega
+ low_power = low_torque * low_omega
+ #if should_print:
+ # print gear_name, "High omega", high_omega, "Low omega", low_omega
+ # print gear_name, "High torque", high_torque, "Low torque", low_torque
+ # print gear_name, "High power", high_power, "Low power", low_power
+
+ # Shift algorithm improvements.
+ # TODO(aschuh):
+ # It takes time to shift. Shifting down for 1 cycle doesn't make sense
+ # because you will end up slower than without shifting. Figure out how
+ # to include that info.
+ # If the driver is still in high gear, but isn't asking for the extra power
+ # from low gear, don't shift until he asks for it.
+ goal_gear_is_high = high_power > low_power
+ #goal_gear_is_high = True
+
+ if not self.IsInGear(current_gear):
+ print gear_name, 'Not in gear.'
+ return current_gear
+ else:
+ is_high = current_gear is VelocityDrivetrain.HIGH
+ if is_high != goal_gear_is_high:
+ if goal_gear_is_high:
+ print gear_name, 'Shifting up.'
+ return VelocityDrivetrain.SHIFTING_UP
+ else:
+ print gear_name, 'Shifting down.'
+ return VelocityDrivetrain.SHIFTING_DOWN
+ else:
+ return current_gear
+
+ def FilterVelocity(self, throttle):
+ # Invert the plant to figure out how the velocity filter would have to work
+ # out in order to filter out the forwards negative inertia.
+ # This math assumes that the left and right power and velocity are equal.
+
+ # The throttle filter should filter such that the motor in the highest gear
+ # should be controlling the time constant.
+ # Do this by finding the index of FF that has the lowest value, and computing
+ # the sums using that index.
+ FF_sum = self.CurrentDrivetrain().FF.sum(axis=1)
+ min_FF_sum_index = numpy.argmin(FF_sum)
+ min_FF_sum = FF_sum[min_FF_sum_index, 0]
+ min_K_sum = self.CurrentDrivetrain().K[min_FF_sum_index, :].sum()
+ # Compute the FF sum for high gear.
+ high_min_FF_sum = self.drivetrain_high_high.FF[0, :].sum()
+
+ # U = self.K[0, :].sum() * (R - x_avg) + self.FF[0, :].sum() * R
+ # throttle * 12.0 = (self.K[0, :].sum() + self.FF[0, :].sum()) * R
+ # - self.K[0, :].sum() * x_avg
+
+ # R = (throttle * 12.0 + self.K[0, :].sum() * x_avg) /
+ # (self.K[0, :].sum() + self.FF[0, :].sum())
+
+ # U = (K + FF) * R - K * X
+ # (K + FF) ^-1 * (U + K * X) = R
+
+ # Scale throttle by min_FF_sum / high_min_FF_sum. This will make low gear
+ # have the same velocity goal as high gear, and so that the robot will hold
+ # the same speed for the same throttle for all gears.
+ adjusted_ff_voltage = numpy.clip(throttle * 12.0 * min_FF_sum / high_min_FF_sum, -12.0, 12.0)
+ return ((adjusted_ff_voltage + self.ttrust * min_K_sum * (self.X[0, 0] + self.X[1, 0]) / 2.0)
+ / (self.ttrust * min_K_sum + min_FF_sum))
+
+ def Update(self, throttle, steering):
+ # Shift into the gear which sends the most power to the floor.
+ # This is the same as sending the most torque down to the floor at the
+ # wheel.
+
+ self.left_gear = self.ComputeGear(self.X[0, 0], should_print=True,
+ current_gear=self.left_gear,
+ gear_name="left")
+ self.right_gear = self.ComputeGear(self.X[1, 0], should_print=True,
+ current_gear=self.right_gear,
+ gear_name="right")
+ if self.IsInGear(self.left_gear):
+ self.left_cim.X[0, 0] = self.MotorRPM(self.left_shifter_position, self.X[0, 0])
+
+ if self.IsInGear(self.right_gear):
+ self.right_cim.X[0, 0] = self.MotorRPM(self.right_shifter_position, self.X[0, 0])
+
+ if self.IsInGear(self.left_gear) and self.IsInGear(self.right_gear):
+ # Filter the throttle to provide a nicer response.
+ fvel = self.FilterVelocity(throttle)
+
+ # Constant radius means that angualar_velocity / linear_velocity = constant.
+ # Compute the left and right velocities.
+ left_velocity = fvel - steering * numpy.abs(fvel)
+ right_velocity = fvel + steering * numpy.abs(fvel)
+
+ # Write this constraint in the form of K * R = w
+ # angular velocity / linear velocity = constant
+ # (left - right) / (left + right) = constant
+ # left - right = constant * left + constant * right
+
+ # (fvel - steering * numpy.abs(fvel) - fvel - steering * numpy.abs(fvel)) /
+ # (fvel - steering * numpy.abs(fvel) + fvel + steering * numpy.abs(fvel)) =
+ # constant
+ # (- 2 * steering * numpy.abs(fvel)) / (2 * fvel) = constant
+ # (-steering * sign(fvel)) = constant
+ # (-steering * sign(fvel)) * (left + right) = left - right
+ # (steering * sign(fvel) + 1) * left + (steering * sign(fvel) - 1) * right = 0
+
+ equality_k = numpy.matrix(
+ [[1 + steering * numpy.sign(fvel), -(1 - steering * numpy.sign(fvel))]])
+ equality_w = 0.0
+
+ self.R[0, 0] = left_velocity
+ self.R[1, 0] = right_velocity
+
+ # Construct a constraint on R by manipulating the constraint on U
+ # Start out with H * U <= k
+ # U = FF * R + K * (R - X)
+ # H * (FF * R + K * R - K * X) <= k
+ # H * (FF + K) * R <= k + H * K * X
+ R_poly = polytope.HPolytope(
+ self.U_poly.H * (self.CurrentDrivetrain().K + self.CurrentDrivetrain().FF),
+ self.U_poly.k + self.U_poly.H * self.CurrentDrivetrain().K * self.X)
+
+ # Limit R back inside the box.
+ self.boxed_R = CoerceGoal(R_poly, equality_k, equality_w, self.R)
+
+ FF_volts = self.CurrentDrivetrain().FF * self.boxed_R
+ self.U_ideal = self.CurrentDrivetrain().K * (self.boxed_R - self.X) + FF_volts
+ else:
+ print 'Not all in gear'
+ if not self.IsInGear(self.left_gear) and not self.IsInGear(self.right_gear):
+ # TODO(austin): Use battery volts here.
+ R_left = self.MotorRPM(self.left_shifter_position, self.X[0, 0])
+ self.U_ideal[0, 0] = numpy.clip(
+ self.left_cim.K * (R_left - self.left_cim.X) + R_left / self.left_cim.Kv,
+ self.left_cim.U_min, self.left_cim.U_max)
+ self.left_cim.Update(self.U_ideal[0, 0])
+
+ R_right = self.MotorRPM(self.right_shifter_position, self.X[1, 0])
+ self.U_ideal[1, 0] = numpy.clip(
+ self.right_cim.K * (R_right - self.right_cim.X) + R_right / self.right_cim.Kv,
+ self.right_cim.U_min, self.right_cim.U_max)
+ self.right_cim.Update(self.U_ideal[1, 0])
+ else:
+ assert False
+
+ self.U = numpy.clip(self.U_ideal, self.U_min, self.U_max)
+
+ # TODO(austin): Model the robot as not accelerating when you shift...
+ # This hack only works when you shift at the same time.
+ if self.IsInGear(self.left_gear) and self.IsInGear(self.right_gear):
+ self.X = self.CurrentDrivetrain().A * self.X + self.CurrentDrivetrain().B * self.U
+
+ self.left_gear, self.left_shifter_position = self.SimShifter(
+ self.left_gear, self.left_shifter_position)
+ self.right_gear, self.right_shifter_position = self.SimShifter(
+ self.right_gear, self.right_shifter_position)
+
+ print "U is", self.U[0, 0], self.U[1, 0]
+ print "Left shifter", self.left_gear, self.left_shifter_position, "Right shifter", self.right_gear, self.right_shifter_position
+
+
+def main(argv):
+ vdrivetrain = VelocityDrivetrain()
+
+ if len(argv) != 5:
+ print "Expected .h file name and .cc file name"
+ else:
+ dog_loop_writer = control_loop.ControlLoopWriter(
+ "VelocityDrivetrain", [vdrivetrain.drivetrain_low_low,
+ vdrivetrain.drivetrain_low_high,
+ vdrivetrain.drivetrain_high_low,
+ vdrivetrain.drivetrain_high_high],
+ namespaces = ["bot3", "control_loops"])
+
+ if argv[1][-3:] == '.cc':
+ dog_loop_writer.Write(argv[2], argv[1])
+ else:
+ dog_loop_writer.Write(argv[1], argv[2])
+
+ cim_writer = control_loop.ControlLoopWriter(
+ "CIM", [drivetrain.CIM()])
+
+ if argv[3][-3:] == '.cc':
+ cim_writer.Write(argv[4], argv[3])
+ else:
+ cim_writer.Write(argv[3], argv[4])
+ return
+
+ vl_plot = []
+ vr_plot = []
+ ul_plot = []
+ ur_plot = []
+ radius_plot = []
+ t_plot = []
+ left_gear_plot = []
+ right_gear_plot = []
+ vdrivetrain.left_shifter_position = 0.0
+ vdrivetrain.right_shifter_position = 0.0
+ vdrivetrain.left_gear = VelocityDrivetrain.LOW
+ vdrivetrain.right_gear = VelocityDrivetrain.LOW
+
+ print "K is", vdrivetrain.CurrentDrivetrain().K
+
+ if vdrivetrain.left_gear is VelocityDrivetrain.HIGH:
+ print "Left is high"
+ else:
+ print "Left is low"
+ if vdrivetrain.right_gear is VelocityDrivetrain.HIGH:
+ print "Right is high"
+ else:
+ print "Right is low"
+
+ for t in numpy.arange(0, 4.0, vdrivetrain.dt):
+ if t < 1.0:
+ vdrivetrain.Update(throttle=1.00, steering=0.0)
+ elif t < 1.2:
+ vdrivetrain.Update(throttle=1.00, steering=0.0)
+ else:
+ vdrivetrain.Update(throttle=1.00, steering=0.0)
+ t_plot.append(t)
+ vl_plot.append(vdrivetrain.X[0, 0])
+ vr_plot.append(vdrivetrain.X[1, 0])
+ ul_plot.append(vdrivetrain.U[0, 0])
+ ur_plot.append(vdrivetrain.U[1, 0])
+ left_gear_plot.append((vdrivetrain.left_gear is VelocityDrivetrain.HIGH) * 2.0 - 10.0)
+ right_gear_plot.append((vdrivetrain.right_gear is VelocityDrivetrain.HIGH) * 2.0 - 10.0)
+
+ fwd_velocity = (vdrivetrain.X[1, 0] + vdrivetrain.X[0, 0]) / 2
+ turn_velocity = (vdrivetrain.X[1, 0] - vdrivetrain.X[0, 0])
+ if abs(fwd_velocity) < 0.0000001:
+ radius_plot.append(turn_velocity)
+ else:
+ radius_plot.append(turn_velocity / fwd_velocity)
+
+ cim_velocity_plot = []
+ cim_voltage_plot = []
+ cim_time = []
+ cim = drivetrain.CIM()
+ R = numpy.matrix([[300]])
+ for t in numpy.arange(0, 0.5, cim.dt):
+ U = numpy.clip(cim.K * (R - cim.X) + R / cim.Kv, cim.U_min, cim.U_max)
+ cim.Update(U)
+ cim_velocity_plot.append(cim.X[0, 0])
+ cim_voltage_plot.append(U[0, 0] * 10)
+ cim_time.append(t)
+ #pylab.plot(cim_time, cim_velocity_plot, label='cim spinup')
+ #pylab.plot(cim_time, cim_voltage_plot, label='cim voltage')
+ #pylab.legend()
+ #pylab.show()
+
+ # TODO(austin):
+ # Shifting compensation.
+
+ # Tighten the turn.
+ # Closed loop drive.
+
+ pylab.plot(t_plot, vl_plot, label='left velocity')
+ pylab.plot(t_plot, vr_plot, label='right velocity')
+ pylab.plot(t_plot, ul_plot, label='left voltage')
+ pylab.plot(t_plot, ur_plot, label='right voltage')
+ pylab.plot(t_plot, radius_plot, label='radius')
+ pylab.plot(t_plot, left_gear_plot, label='left gear high')
+ pylab.plot(t_plot, right_gear_plot, label='right gear high')
+ pylab.legend()
+ pylab.show()
+ return 0
+
+if __name__ == '__main__':
+ sys.exit(main(sys.argv))
diff --git a/bot3/control_loops/python/polydrivetrain_test.py b/bot3/control_loops/python/polydrivetrain_test.py
new file mode 100755
index 0000000..434cdca
--- /dev/null
+++ b/bot3/control_loops/python/polydrivetrain_test.py
@@ -0,0 +1,82 @@
+#!/usr/bin/python
+
+import polydrivetrain
+import numpy
+from numpy.testing import *
+import polytope
+import unittest
+
+__author__ = 'Austin Schuh (austin.linux@gmail.com)'
+
+
+class TestVelocityDrivetrain(unittest.TestCase):
+ def MakeBox(self, x1_min, x1_max, x2_min, x2_max):
+ H = numpy.matrix([[1, 0],
+ [-1, 0],
+ [0, 1],
+ [0, -1]])
+ K = numpy.matrix([[x1_max],
+ [-x1_min],
+ [x2_max],
+ [-x2_min]])
+ return polytope.HPolytope(H, K)
+
+ def test_coerce_inside(self):
+ """Tests coercion when the point is inside the box."""
+ box = self.MakeBox(1, 2, 1, 2)
+
+ # x1 = x2
+ K = numpy.matrix([[1, -1]])
+ w = 0
+
+ assert_array_equal(polydrivetrain.CoerceGoal(box, K, w,
+ numpy.matrix([[1.5], [1.5]])),
+ numpy.matrix([[1.5], [1.5]]))
+
+ def test_coerce_outside_intersect(self):
+ """Tests coercion when the line intersects the box."""
+ box = self.MakeBox(1, 2, 1, 2)
+
+ # x1 = x2
+ K = numpy.matrix([[1, -1]])
+ w = 0
+
+ assert_array_equal(polydrivetrain.CoerceGoal(box, K, w, numpy.matrix([[5], [5]])),
+ numpy.matrix([[2.0], [2.0]]))
+
+ def test_coerce_outside_no_intersect(self):
+ """Tests coercion when the line does not intersect the box."""
+ box = self.MakeBox(3, 4, 1, 2)
+
+ # x1 = x2
+ K = numpy.matrix([[1, -1]])
+ w = 0
+
+ assert_array_equal(polydrivetrain.CoerceGoal(box, K, w, numpy.matrix([[5], [5]])),
+ numpy.matrix([[3.0], [2.0]]))
+
+ def test_coerce_middle_of_edge(self):
+ """Tests coercion when the line intersects the middle of an edge."""
+ box = self.MakeBox(0, 4, 1, 2)
+
+ # x1 = x2
+ K = numpy.matrix([[-1, 1]])
+ w = 0
+
+ assert_array_equal(polydrivetrain.CoerceGoal(box, K, w, numpy.matrix([[5], [5]])),
+ numpy.matrix([[2.0], [2.0]]))
+
+ def test_coerce_perpendicular_line(self):
+ """Tests coercion when the line does not intersect and is in quadrant 2."""
+ box = self.MakeBox(1, 2, 1, 2)
+
+ # x1 = -x2
+ K = numpy.matrix([[1, 1]])
+ w = 0
+
+ assert_array_equal(polydrivetrain.CoerceGoal(box, K, w, numpy.matrix([[5], [5]])),
+ numpy.matrix([[1.0], [1.0]]))
+
+
+if __name__ == '__main__':
+ unittest.main()