176 lines
7.3 KiB
Python
176 lines
7.3 KiB
Python
import numpy as np
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import time
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from casadi_opt import OpenLoopSolver
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class MPCController:
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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.mstep = 2
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self.ols = OpenLoopSolver(N=20, T=1.0)
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self.ols.setup()
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self.dt = self.ols.T / self.ols.N
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# integrator
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self.omega_max = 5.0
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self.control_scaling = 0.4
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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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target_pos = np.array([0.0, 0.0, 0.0])
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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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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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else:
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print("enable control")
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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 == 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 == 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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# print(np.linalg.norm(x_pred-target_pos))
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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] * self.control_scaling
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us2 = res[1] * self.control_scaling
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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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time.sleep(self.dt - dt_mpc)
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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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def get_measurement(self, robot_id):
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return self.estimator.get_robot_state_estimate(robot_id)
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