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rewrote pid controller to derive from ControllerBase. works with multiple robots now

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This commit is contained in:
Simon Pirkelmann 2020-11-14 16:41:16 +01:00
parent 2060d8eb15
commit 35b6c3bdfd
1 changed files with 82 additions and 156 deletions
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238
remote_control/pid_controller.py
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@ -2,9 +2,12 @@ import numpy as np
import math import math
import time import time
class PIDController: from controller import ControllerBase
def __init__(self, event_listener):
self.t = time.time() class PIDController(ControllerBase):
def __init__(self):
super().__init__()
self.t = None
self.controlling = False self.controlling = False
@ -16,177 +19,100 @@ class PIDController:
self.I_pos = 0.3 self.I_pos = 0.3
self.D_pos = 0.1 self.D_pos = 0.1
self.mode = None self.mode = 'combined'
self.event_listener = event_listener self.e_angle_old = 0.0
self.e_pos_old = 0.0
def control_multiple_robots(self, robots): self.i = 0.0
robot_target_positions = {} self.i_angle = 0.0
for robot in robots: self.i_pos = 0.0
robot_target_positions[robot.id] = None
running = True def set_target_position(self, target_pos):
while running: super(PIDController, self).set_target_position(target_pos)
pass self.mode = 'combined'
self.e_angle_old = 0.0
self.e_pos_old = 0.0
self.i = 0.0
self.i_angle = 0.0
self.i_pos = 0.0
def interactive_control(self, robots): def compute_control(self, state):
controlled_robot_number = 0 # measure state
robot = robots[controlled_robot_number] x_pred = state[1:]
ts = [] if self.t is None:
angles = [] dt = 0.1
else:
dt = time.time() - self.t
target_pos = np.array([0.0, 0.0, 0.0]) #print(f"x_pred = {x_pred}\ntarget = {target_pos}\nerror = {np.linalg.norm(target_pos - x_pred)}\n")
e_angle_old = 0.0 if self.mode == 'angle':
e_pos_old = 0.0 # compute angle such that robot faces to target point
target_angle = self.target_pos[2]
i = 0.0 angles_unwrapped = np.unwrap([x_pred[2], target_angle]) # unwrap angle to avoid jump in data
i_angle = 0.0
i_pos = 0.0
t0 = time.time() e_angle = angles_unwrapped[0] - angles_unwrapped[1] # angle difference
p = self.P_angle * e_angle
# i += self.I * dt * e # right Riemann sum
self.i += self.I_angle * dt * (e_angle + self.e_angle_old)/2.0 # trapezoidal rule
d = self.D_angle * (e_angle - self.e_angle_old)/dt
running = True u1 = p + self.i + d
while running: u2 = - u1
# handle events from opencv window
while not self.event_listener.event_queue.empty():
event = self.event_listener.event_queue.get()
print("event: ", event)
if event[0] == 'click':
target = event[1]
target_pos = np.array([target['x'], target['y'], target['angle']])
i_angle = 0
i_pos = 0
e_pos_old = 0
e_angle_old = 0
self.mode = 'combined'
elif event[0] == 'key':
key = event[1]
if key == 32: # arrow up self.e_angle_old = e_angle
self.controlling = not self.controlling
if not self.controlling:
print("disable control")
robot.send_cmd() # stop robot
self.mode = None # reset control mode
else:
print("enable control")
i = 0
self.t = time.time()
elif key == 48: # 0
target_pos = np.array([0.0, 0.0, 0.0]) # TODO: use center of board for target pos
elif key == 97: # a
self.mode = 'angle'
e_angle_old = 0
i = 0
self.t = time.time()
elif key == 99: # c
self.mode = 'combined'
e_angle_old = 0
e_pos_old = 0
i_angle = 0
i_pos = 0
self.t = time.time()
elif key == 112: # p
self.mode = 'position'
e_pos_old = 0
i = 0
self.t = time.time()
elif key == 43: # +
self.P_pos += 0.1
print("P = ", self.P_angle)
elif key == 45: # -
self.P_pos -= 0.1
print("P = ", self.P_angle)
elif key == 9: # TAB
# switch controlled robot
robot.send_cmd() # stop current robot
controlled_robot_number = (controlled_robot_number + 1) % len(robots)
robot = robots[controlled_robot_number]
print(f"controlled robot: {robot.id}")
elif key == 113 or key == 27: # q or ESCAPE
print("quit!")
self.controlling = False
robot.send_cmd()
return
if self.controlling: elif self.mode == 'combined':
# measure state # compute angle such that robot faces to target point
t, x, y, angle = robot.get_measurement() v = self.target_pos[0:2] - x_pred[0:2]
x_pred = np.array([x, y, angle]) target_angle = math.atan2(v[1], v[0])
dt = self.t - time.time()
#print(f"x_pred = {x_pred}\ntarget = {target_pos}\nerror = {np.linalg.norm(target_pos - x_pred)}\n") angles_unwrapped = np.unwrap([x_pred[2], target_angle]) # unwrap angle to avoid jump in data
if self.mode == 'angle':
# compute angle such that robot faces to target point
target_angle = target_pos[2]
ts.append(time.time() - t0) e_angle = angles_unwrapped[0] - angles_unwrapped[1] # angle difference
angles.append(x_pred[2]) e_pos = np.linalg.norm(v)
angles_unwrapped = np.unwrap([x_pred[2], target_angle]) # unwrap angle to avoid jump in data if e_pos < 0.05:
self.mode = 'angle'
self.e_angle_old = 0
self.e_pos_old = 0
self.i_angle = 0
self.i_pos = 0
u1 = 0
u2 = 0
else:
forward = abs(e_angle) < np.pi/2.0
e_angle = angles_unwrapped[0] - angles_unwrapped[1] # angle difference if not forward:
p = self.P_angle * e_angle if e_angle > np.pi/2.0:
# i += self.I * dt * e # right Riemann sum e_angle -= np.pi
i += self.I_angle * dt * (e_angle + e_angle_old)/2.0 # trapezoidal rule
d = self.D_angle * (e_angle - e_angle_old)/dt
u1 = p - i - d
u2 = - u1
e_angle_old = e_angle
elif self.mode == 'combined':
# compute angle such that robot faces to target point
v = target_pos[0:2] - x_pred[0:2]
target_angle = math.atan2(v[1], v[0])
angles_unwrapped = np.unwrap([x_pred[2], target_angle]) # unwrap angle to avoid jump in data
e_angle = angles_unwrapped[0] - angles_unwrapped[1] # angle difference
e_pos = np.linalg.norm(v)
if e_pos < 0.05:
self.mode = 'angle'
e_angle_old = 0
e_pos_old = 0
i_angle = 0
i_pos = 0
u1 = 0
u2 = 0
else: else:
forward = abs(e_angle) < np.pi/2.0 e_angle += np.pi
if not forward: p_angle = self.P_angle * e_angle
if e_angle > np.pi/2.0: self.i_angle += self.I_angle * dt * (e_angle + self.e_angle_old) / 2.0 # trapezoidal rule
e_angle -= np.pi d_angle = self.D_angle * (e_angle - self.e_angle_old) / dt
else:
e_angle += np.pi
p_angle = self.P_angle * e_angle p_pos = self.P_pos * e_pos
i_angle += self.I_angle * dt * (e_angle + e_angle_old) / 2.0 # trapezoidal rule self.i_pos += self.I_pos * dt * (e_pos + self.e_pos_old) / 2.0 # trapezoidal rule
d_angle = self.D_angle * (e_angle - e_angle_old) / dt d_pos = self.D_pos * (e_pos - self.e_pos_old) / dt
p_pos = self.P_pos * e_pos
i_pos += self.I_pos * dt * (e_pos + e_pos_old) / 2.0 # trapezoidal rule
d_pos = self.D_pos * (e_pos - e_pos_old) / dt
if forward:
u1 = p_angle + p_pos - i_angle - i_pos - d_angle - d_pos
u2 = - p_angle + p_pos + i_angle - i_pos + d_angle - d_pos
else:
u1 = p_angle - p_pos - i_angle + i_pos - d_angle + d_pos
u2 = - p_angle - p_pos + i_angle + i_pos + d_angle + d_pos
e_pos_old = e_pos
e_angle_old = e_angle
if forward:
u1 = p_angle + p_pos + self.i_angle + self.i_pos + d_angle + d_pos
u2 = - p_angle + p_pos - self.i_angle + self.i_pos - d_angle + d_pos
else: else:
u1 = 0.0 u1 = p_angle - p_pos + self.i_angle - self.i_pos + d_angle - d_pos
u2 = 0.0 u2 = - p_angle - p_pos - self.i_angle - self.i_pos - d_angle - d_pos
#print(f"u = ({u1}, {u2})")
robot.send_cmd(u1, u2) self.e_pos_old = e_pos
self.t = time.time() # save time when the most recent control was applied self.e_angle_old = e_angle
time.sleep(0.05)
else:
u1 = 0.0
u2 = 0.0
#print(f"u = ({u1}, {u2})")
self.t = time.time() # save time when the most recent control was applied
return (u1, u2)