working PID controller for driving forwards
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remote_control/pid_controller.py
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292
remote_control/pid_controller.py
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import numpy as np
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import math
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import time
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class PIDController:
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def __init__(self, estimator):
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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.P_angle = 0.5 # PID parameters determined by Ziegler-Nichols. measured K_crit = 1.4, T_crit = 1.5
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self.I_angle = 0.4
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self.D_angle = 0.1
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self.P_pos = 0.50
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self.I_pos = 0.3
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self.D_pos = 0.1
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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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near_target = 0
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while near_target < near_target_counter:
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while not self.estimator.event_queue.empty():
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event = self.estimator.event_queue.get()
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print("event: ", event)
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if event[0] == 'click':
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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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controlled_robot_number = 0
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robot = robots[controlled_robot_number]
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ts = []
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angles = []
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target_pos = np.array([0.0, 0.0, 0.0])
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e_angle_old = 0.0
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e_pos_old = 0.0
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i = 0.0
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i_angle = 0.0
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i_pos = 0.0
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t0 = time.time()
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running = True
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while running:
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# handle events from opencv window
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while not self.estimator.event_queue.empty():
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event = self.estimator.event_queue.get()
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print("event: ", event)
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if event[0] == 'click':
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target_pos = event[1]
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i_angle = 0
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i_pos = 0
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e_pos_old = 0
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e_angle_old = 0
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self.mode = 'combined'
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elif event[0] == 'key':
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key = event[1]
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if key == 32: # arrow up
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self.controlling = not self.controlling
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if not self.controlling:
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print("disable control")
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robot.send_cmd() # stop robot
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self.mode = None # reset control mode
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else:
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print("enable control")
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i = 0
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self.t = time.time()
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elif key == 48: # 0
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target_pos = np.array([0.0, 0.0, 0.0]) # TODO: use center of board for target pos
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elif key == 97: # a
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self.mode = 'angle'
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e_angle_old = 0
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i = 0
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self.t = time.time()
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elif key == 99: # c
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self.mode = 'combined'
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e_angle_old = 0
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e_pos_old = 0
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i_angle = 0
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i_pos = 0
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self.t = time.time()
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elif key == 112: # p
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self.mode = 'position'
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e_pos_old = 0
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i = 0
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self.t = time.time()
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elif key == 43: # +
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self.P_pos += 0.1
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print("P = ", self.P_angle)
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elif key == 45: # -
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self.P_pos -= 0.1
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print("P = ", self.P_angle)
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elif key == 9: # TAB
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# switch controlled robot
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robot.send_cmd() # stop current robot
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controlled_robot_number = (controlled_robot_number + 1) % len(robots)
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robot = robots[controlled_robot_number]
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print(f"controlled robot: {robot.id}")
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elif key == 113 or key == 27: # q or 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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if self.controlling:
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# measure state
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x_pred = self.get_measurement(robot.id)
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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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if self.mode == 'angle':
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# compute angle such that robot faces to target point
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target_angle = target_pos[2]
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ts.append(time.time() - t0)
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angles.append(x_pred[2])
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angles_unwrapped = np.unwrap([x_pred[2], target_angle]) # unwrap angle to avoid jump in data
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e_angle = angles_unwrapped[0] - angles_unwrapped[1] # angle difference
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p = self.P_angle * e_angle
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# i += self.I * dt * e # right Riemann sum
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i += self.I_angle * dt * (e_angle + e_angle_old)/2.0 # trapezoidal rule
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d = self.D_angle * (e_angle - e_angle_old)/dt
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u1 = p - i - d
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u2 = - u1
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e_angle_old = e_angle
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elif self.mode == 'position':
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# we assume that we face straight to the target position
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v = target_pos[0:2] - x_pred[0:2]
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# check if robot faces in same direction as pos-target-vector
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target_angle = math.atan2(v[1], v[0])
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current_angle = x_pred[2]
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angles_unwrapped = np.unwrap([current_angle, target_angle]) # unwrap angle to avoid jump in data
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alpha = angles_unwrapped[0]
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beta = angles_unwrapped[1]
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epsilon = 0.3
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if not (abs(alpha - beta) < epsilon or abs(alpha - beta + np.pi) < epsilon or abs(alpha - beta - np.pi) < epsilon):
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print("switch to angle mode")
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u1 = u2 = 0.0
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robot.send_cmd(u1, u2)
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self.mode = 'angle'
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elif abs(alpha - beta) < epsilon:
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# forward
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e = np.linalg.norm(x_pred[0:2] - target_pos[0:2]) # compute euclidean distance to target
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p = self.P * e
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# i += self.I * dt * e # right Riemann sum
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i += self.I * dt * (e + e_old) / 2.0 # trapezoidal rule
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d = self.D * (e - e_old) / dt
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u1 = p - i + d
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u2 = u1
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else:
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# backward
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e = np.linalg.norm(x_pred[0:2] - target_pos[0:2]) # compute euclidean distance to target
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p = self.P * e
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# i += self.I * dt * e # right Riemann sum
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i += self.I * dt * (e + e_old) / 2.0 # trapezoidal rule
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d = self.D * (e - e_old) / dt
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u1 = -(p - i + d)
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u2 = u1
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e_old = e
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#else:
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# print("switching to angle mode")
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# u1 = u2 = 0.0
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# self.mode = 'angle'
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elif self.mode == 'combined':
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# compute angle such that robot faces to target point
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v = target_pos[0:2] - x_pred[0:2]
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target_angle = math.atan2(v[1], v[0])
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angles_unwrapped = np.unwrap([x_pred[2], target_angle]) # unwrap angle to avoid jump in data
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e_angle = angles_unwrapped[0] - angles_unwrapped[1] # angle difference
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e_pos = np.linalg.norm(v)
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p_angle = self.P_angle * e_angle
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i_angle += self.I_angle * dt * (e_angle + e_angle_old) / 2.0 # trapezoidal rule
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d_angle = self.D_angle * (e_angle - e_angle_old) / dt
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p_pos = self.P_pos * e_pos
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i_pos += self.I_pos * dt * (e_pos + e_pos_old) / 2.0 # trapezoidal rule
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d_pos = self.D_pos * (e_pos - e_pos_old) / dt
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u1 = p_angle + p_pos - i_angle - i_pos
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u2 = - p_angle + p_pos + i_angle - i_pos
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e_pos_old = e_pos
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e_angle_old = e_angle
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if e_pos < 0.05:
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self.mode = 'angle'
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else:
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u1 = 0.0
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u2 = 0.0
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#print(f"u = ({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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time.sleep(0.1)
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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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