forked from Telos4/RoboRally
about to make changes s.t. PID controller derives from ControllerBase class
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@ -2,15 +2,13 @@ import numpy as np
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import math
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import math
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import time
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import time
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class PIDController:
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class PIDController:
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def __init__(self, estimator):
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def __init__(self, event_listener):
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self.t = time.time()
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self.t = time.time()
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self.estimator = estimator
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self.controlling = False
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self.controlling = False
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self.P_angle = 0.4 # PID parameters determined by Ziegler-Nichols. measured K_crit = 1.4, T_crit = 1.5
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self.P_angle = 0.4
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self.I_angle = 0.35
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self.I_angle = 0.35
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self.D_angle = 0.1
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self.D_angle = 0.1
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@ -20,84 +18,17 @@ class PIDController:
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self.mode = None
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self.mode = None
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def move_to_pos(self, target_pos, robot, near_target_counter=5):
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self.event_listener = event_listener
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near_target = 0
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while near_target < near_target_counter:
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def control_multiple_robots(self, robots):
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while not self.estimator.event_queue.empty():
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robot_target_positions = {}
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event = self.estimator.event_queue.get()
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for robot in robots:
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print("event: ", event)
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robot_target_positions[robot.id] = None
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if event[0] == 'click':
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running = True
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while running:
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pass
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pass
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elif event[0] == 'key':
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key = event[1]
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if key == 84: # arrow up
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self.controlling = True
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self.t = time.time()
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elif key == 82: # arrow down
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self.controlling = False
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robot.send_cmd()
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elif key == 48: # 0
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target_pos = np.array([0.0, 0.0, 0.0])
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elif key == 43: # +
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self.control_scaling += 0.1
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self.control_scaling = min(self.control_scaling, 1.0)
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print("control scaling = ", self.control_scaling)
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elif key == 45: # -
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self.control_scaling -= 0.1
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self.control_scaling = max(self.control_scaling, 0.1)
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print("control scaling = ", self.control_scaling)
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elif key == 113:
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print("quit!")
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self.controlling = False
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robot.send_cmd()
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return
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elif key == 27: # escape
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print("quit!")
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self.controlling = False
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robot.send_cmd()
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return
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x_pred = self.get_measurement(robot.id)
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if x_pred is not None:
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error_pos = np.linalg.norm(x_pred[0:2] - target_pos[0:2])
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angles_unwrapped = np.unwrap([x_pred[2], target_pos[2]]) # unwrap angle to avoid jump in data
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error_ang = np.abs(angles_unwrapped[0] - angles_unwrapped[1])
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# print("error pos = ", error_pos)
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# print("error_pos = {}, error_ang = {}".format(error_pos, error_ang))
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# if error_pos > 0.075 or error_ang > 0.35:
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if error_pos > 0.05 or error_ang > 0.1:
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# solve mpc open loop problem
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res = self.ols.solve(x_pred, target_pos)
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# us1 = res[0]
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# us2 = res[1]
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us1 = res[0] * self.control_scaling
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us2 = res[1] * self.control_scaling
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# print("u = {}", (us1, us2))
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# print("---------------- mpc solution took {} seconds".format(tmpc_end - tmpc_start))
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dt_mpc = time.time() - self.t
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if dt_mpc < self.dt: # wait until next control can be applied
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# print("sleeping for {} seconds...".format(self.dt - dt_mpc))
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time.sleep(self.dt - dt_mpc)
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else:
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us1 = [0] * self.mstep
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us2 = [0] * self.mstep
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near_target += 1
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# send controls to the robot
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for i in range(0, self.mstep): # option to use multistep mpc if len(range) > 1
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u1 = us1[i]
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u2 = us2[i]
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robot.send_cmd(u1, u2)
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if i < self.mstep:
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time.sleep(self.dt)
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self.t = time.time() # save time the most recent control was applied
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else:
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print("robot not detected yet!")
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def interactive_control(self, robots):
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def interactive_control(self, robots):
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controlled_robot_number = 0
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controlled_robot_number = 0
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@ -119,11 +50,12 @@ class PIDController:
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running = True
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running = True
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while running:
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while running:
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# handle events from opencv window
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# handle events from opencv window
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while not self.estimator.event_queue.empty():
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while not self.event_listener.event_queue.empty():
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event = self.estimator.event_queue.get()
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event = self.event_listener.event_queue.get()
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print("event: ", event)
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print("event: ", event)
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if event[0] == 'click':
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if event[0] == 'click':
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target_pos = event[1]
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target = event[1]
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target_pos = np.array([target['x'], target['y'], target['angle']])
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i_angle = 0
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i_angle = 0
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i_pos = 0
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i_pos = 0
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e_pos_old = 0
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e_pos_old = 0
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@ -181,11 +113,10 @@ class PIDController:
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if self.controlling:
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if self.controlling:
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# measure state
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# measure state
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x_pred = self.get_measurement(robot.id)
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t, x, y, angle = robot.get_measurement()
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x_pred = np.array([x, y, angle])
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dt = self.t - time.time()
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dt = self.t - time.time()
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#print(f"x_pred = {x_pred}\ntarget = {target_pos}\nerror = {np.linalg.norm(target_pos - x_pred)}\n")
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#print(f"x_pred = {x_pred}\ntarget = {target_pos}\nerror = {np.linalg.norm(target_pos - x_pred)}\n")
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if self.mode == 'angle':
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if self.mode == 'angle':
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# compute angle such that robot faces to target point
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# compute angle such that robot faces to target point
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@ -243,11 +174,9 @@ class PIDController:
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d_pos = self.D_pos * (e_pos - e_pos_old) / dt
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d_pos = self.D_pos * (e_pos - e_pos_old) / dt
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if forward:
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if forward:
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print("forward")
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u1 = p_angle + p_pos - i_angle - i_pos - d_angle - d_pos
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u1 = p_angle + p_pos - i_angle - i_pos - d_angle - d_pos
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u2 = - p_angle + p_pos + i_angle - i_pos + d_angle - d_pos
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u2 = - p_angle + p_pos + i_angle - i_pos + d_angle - d_pos
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else:
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else:
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print("backward")
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u1 = p_angle - p_pos - i_angle + i_pos - d_angle + d_pos
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u1 = p_angle - p_pos - i_angle + i_pos - d_angle + d_pos
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u2 = - p_angle - p_pos + i_angle + i_pos + d_angle + d_pos
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u2 = - p_angle - p_pos + i_angle + i_pos + d_angle + d_pos
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@ -260,8 +189,4 @@ class PIDController:
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#print(f"u = ({u1}, {u2})")
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#print(f"u = ({u1}, {u2})")
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robot.send_cmd(u1, u2)
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robot.send_cmd(u1, u2)
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self.t = time.time() # save time when the most recent control was applied
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self.t = time.time() # save time when the most recent control was applied
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time.sleep(0.1)
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time.sleep(0.05)
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def get_measurement(self, robot_id):
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return self.estimator.get_robot_state_estimate(robot_id)
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