fixed problem with target angle of -pi, pi which was caused by discontinuities in the angle measurements
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@ -3,6 +3,9 @@ import time
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# look at: https://github.com/casadi/casadi/blob/master/docs/examples/python/vdp_indirect_multiple_shooting.py
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# TODO for roborally
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# implement essential movements: forward, backward, turn left, turn right on grid-based level
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class OpenLoopSolver:
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def __init__(self, N=20, T=4.0):
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self.T = T
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@ -40,7 +43,8 @@ class OpenLoopSolver:
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# cost functional
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target = (-0.0, 0.0)
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L = (x-target[0]) ** 2 + (y-target[1]) ** 2 + 1e-2 * theta ** 2 + 1e-2 * (omegar ** 2 + omegal ** 2)
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L = (x-target[0]) ** 2 + (y-target[1]) ** 2 + 1e-2 * (omegar ** 2 + omegal ** 2)
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#L = (x-target[0]) ** 2 + (y-target[1]) ** 2 + 1e-2 * theta ** 2 + 1e-2 * (omegar ** 2 + omegal ** 2)
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# Fixed step Runge-Kutta 4 integrator
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M = 4 # RK4 steps per interval
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@ -138,7 +142,12 @@ class OpenLoopSolver:
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#return
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def solve(self, x0, target, obstacles, track):
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def solve(self, x0, target, obstacles, turn=False):
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angles_unwrapped = np.unwrap([x0[2], target[2]]) # unwrap angle to avoid jump in data
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x0[2] = angles_unwrapped[0]
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target[2] = angles_unwrapped[1]
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tstart = time.time()
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# alternative solution using multiple shooting (way faster!)
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self.opti = Opti() # Optimization problem
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@ -186,7 +195,11 @@ class OpenLoopSolver:
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theta)) * omegal * omega_max
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f3 = -(r / (2 * R) * omegar - r / (2 * R) * omegal) * omega_max
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xdot = vertcat(f1, f2, f3)
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L = (x - target[0]) ** 2 + (y - target[1]) ** 2 + 1e-2 * (theta - target[2]) ** 2 + 1e-2 * (omegar ** 2 + omegal ** 2)
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#L = 100 * (x - target[0]) ** 2 + 100 * (y - target[1]) ** 2 + 1e-1 * (theta - target[2])**2 + 1e-2 * (omegar ** 2 + omegal ** 2)
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L = 100 * (x - target[0]) ** 2 + 100 * (y - target[1]) ** 2 + 1e-1 * (target[2] - theta) ** 2 + 1e-2 * (
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omegar ** 2 + omegal ** 2)
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#L = (x - target[0]) ** 2 + (y - target[1]) ** 2 + 1e-2 * (theta - target[2]) ** 2 + 1e-2 * (omegar ** 2 + omegal ** 2)
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self.f = Function('f', [state, control], [xdot, L])
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# ---- solve NLP ------
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@ -222,7 +235,7 @@ class OpenLoopSolver:
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self.opti.subject_to(self.Q[:, 0] == 0.0)
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solver = self.opti.solver("ipopt", {}, {"print_level": 0})
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solver = self.opti.solver("ipopt", {'print_time': False}, {"print_level": 0})
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# ---- misc. constraints ----------
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# self.opti.subject_to(X[1,:]>=0) # Time must be positive
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@ -256,7 +269,7 @@ class OpenLoopSolver:
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self.opti.subject_to(angle[0] == x0[2]) # finish line at position 1
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tend = time.time()
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print("setting up problem took {} seconds".format(tend - tstart))
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#print("setting up problem took {} seconds".format(tend - tstart))
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tstart = time.time()
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if self.use_warmstart and self.opti_x0 is not None:
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@ -267,7 +280,7 @@ class OpenLoopSolver:
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print("could not set warmstart")
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sol = self.opti.solve() # actual solve
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tend = time.time()
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print("solving the problem took {} seconds".format(tend - tstart))
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#print("solving the problem took {} seconds".format(tend - tstart))
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tstart = time.time()
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self.opti_x0 = sol.value(self.opti.x)
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@ -278,7 +291,7 @@ class OpenLoopSolver:
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u_opt_1 = sol.value(self.U[0,:])
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u_opt_2 = sol.value(self.U[1,:])
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tend = time.time()
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print("postprocessing took {} seconds".format(tend - tstart))
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#print("postprocessing took {} seconds".format(tend - tstart))
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return (u_opt_1, u_opt_2, sol.value(posx), sol.value(posy))
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@ -408,7 +408,6 @@ class RemoteController:
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elif event.key == pygame.K_w:
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move = 1
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turn = 0
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elif event.key == pygame.K_s:
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move = -1
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turn = 0
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@ -419,7 +418,8 @@ class RemoteController:
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elif event.key == pygame.K_d:
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turn = -1
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move = 0
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integ = 0
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elif event.key == pygame.K_r:
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turn = 2
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elif event.key == pygame.K_p:
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self.pid = True
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elif event.key == pygame.K_SPACE:
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@ -459,7 +459,7 @@ class RemoteController:
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elif turn != 0:
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new_x = grid_pos[0]
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new_y = grid_pos[1]
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new_angle = grid_pos[2] + turn * np.pi/2
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new_angle = np.unwrap([0, grid_pos[2] + turn * np.pi/2])[1]
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grid_pos = (new_x, new_y, new_angle)
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print(grid_pos)
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@ -473,15 +473,16 @@ class RemoteController:
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tmpc_start = time.time()
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error_pos = np.linalg.norm(x_pred[0:2] - self.target[0:2])
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error_ang = np.abs(x_pred[2] - self.target[2])
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print("error pos = ", error_pos, " error_ang = ", error_ang)
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angles_unwrapped = np.unwrap([x_pred[2], self.target[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_ang = {}, target = {}, angle = {}".format(error_ang, self.target[2], x_pred[2]))
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turning = turn != 0
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if error_pos > 0.1 or error_ang > 0.4:
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# solve mpc open loop problem
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res = self.ols.solve(x_pred, self.target, [], turning)
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#us1 = res[0]
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#us2 = res[1]
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us1 = res[0] * control_scaling
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