forked from Telos4/RoboRally
309 lines
11 KiB
Python
309 lines
11 KiB
Python
from casadi import *
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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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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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self.N = N
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self.opti_x0 = None
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self.opti_lam_g0 = None
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self.use_warmstart = True
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def setup(self):
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x = SX.sym('x')
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y = SX.sym('y')
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theta = SX.sym('theta')
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state = vertcat(x, y, theta)
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r = 0.03
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R = 0.05
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d = 0.02
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#omega_max = 13.32
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omega_max = 10.0
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omegar = SX.sym('omegar')
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omegal = SX.sym('omegal')
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control = vertcat(omegar, omegal)
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# model equation
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f1 = (r / 2 * cos(theta) - r * d / (2 * R) * sin(theta)) * omegar * omega_max + (r / 2 * cos(theta) + r * d / (2 * R) * sin(
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theta)) * omegal * omega_max
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f2 = (r / 2 * sin(theta) + r * d / (2 * R) * cos(theta)) * omegar * omega_max + (r / 2 * sin(theta) - r * d / (2 * R) * cos(
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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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f = Function('f', [x, y, theta, omegar, omegal], [f1, f2, f3])
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print("f = {}".format(f))
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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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# Fixed step Runge-Kutta 4 integrator
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M = 4 # RK4 steps per interval
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DT = self.T / self.N / M
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print("DT = {}".format(DT))
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self.f = Function('f', [state, control], [xdot, L])
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X0 = MX.sym('X0', 3)
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U = MX.sym('U', 2)
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X = X0
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Q = 0
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runge_kutta = True
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if runge_kutta:
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for j in range(M):
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k1, k1_q = self.f(X, U)
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k2, k2_q = self.f(X + DT / 2 * k1, U)
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k3, k3_q = self.f(X + DT / 2 * k2, U)
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k4, k4_q = self.f(X + DT * k3, U)
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X = X + DT / 6 * (k1 + 2 * k2 + 2 * k3 + k4)
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Q = Q + DT / 6 * (k1_q + 2 * k2_q + 2 * k3_q + k4_q)
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else:
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DT = self.T / self.N
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k1, k1_q = f(X, U)
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X = X + DT * k1
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Q = Q + DT * k1_q
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F = Function('F', [X0, U], [X, Q], ['x0', 'p'], ['xf', 'qf'])
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# F_euler = Function('F_euler', [X0, U], [Xeuler, Qeuler], ['x0', 'p'], ['xf', 'qf'])
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Fk = F(x0=[0.2, 0.3, 0.0], p=[-1.1, 1.1])
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print(Fk['xf'])
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print(Fk['qf'])
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# Start with an empty NLP
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w = []
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w0 = []
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lbw = []
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ubw = []
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J = 0
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g = []
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lbg = []
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ubg = []
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# Formulate the NLP
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# Xk = MX(x0)
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# for k in range(self.N):
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# # New NLP variable for the control
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# U1k = MX.sym('U1_' + str(k), 2)
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# # U2k = MX.sym('U2_' + str(k))
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# w += [U1k]
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# lbw += [-0.5, -0.5]
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# ubw += [0.5, 0.5]
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# w0 += [0, 0]
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#
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# # Integrate till the end of the interval
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# Fk = F(x0=Xk, p=U1k)
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# Xk = Fk['xf']
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# J = J + Fk['qf']
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#
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# # Add inequality constraint
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# # g += [Xk[1]]
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# # lbg += [-.0]
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# # ubg += [inf]
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#
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# # Create an NLP solver
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# prob = {'f': J, 'x': vertcat(*w), 'g': vertcat(*g)}
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# self.solver = nlpsol('solver', 'ipopt', prob)
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# Solve the NLP
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if False:
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sol = self.solver(x0=w0, lbx=lbw, ubx=ubw, lbg=lbg, ubg=ubg)
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w_opt = sol['x']
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# Plot the solution
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u_opt = w_opt
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x_opt = [self.x0]
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for k in range(self.N):
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Fk = F(x0=x_opt[-1], p=u_opt[2*k:2*k+2])
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x_opt += [Fk['xf'].full()]
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x1_opt = [r[0] for r in x_opt]
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x2_opt = [r[1] for r in x_opt]
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x3_opt = [r[2] for r in x_opt]
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tgrid = [self.T/self.N*k for k in range(self.N+1)]
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#import matplotlib.pyplot as plt
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#plt.figure(2)
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#plt.clf()
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#plt.plot(tgrid, x1_opt, '--')
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#plt.plot(tgrid, x2_opt, '-')
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#plt.plot(tgrid, x3_opt, '*')
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#plt.step(tgrid, vertcat(DM.nan(1), u_opt), '-.')
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#plt.xlabel('t')
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#plt.legend(['x1','x2','x3','u'])
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#plt.grid()
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#plt.show()
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#return
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def solve(self, x0, target, obstacles):
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# alternative solution using multiple shooting (way faster!)
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self.opti = Opti() # Optimization problem
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# ---- decision variables ---------
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self.X = self.opti.variable(3,self.N+1) # state trajectory
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self.Q = self.opti.variable(1,self.N+1) # state trajectory
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self.U = self.opti.variable(2,self.N) # control trajectory (throttle)
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self.slack = self.opti.variable(1,1)
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#T = self.opti.variable() # final time
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# ---- objective ---------
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#self.opti.minimize(T) # race in minimal time
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# ---- dynamic constraints --------
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#f = lambda x,u: vertcat(f1, f2, f3) # dx/dt = f(x,u)
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# ---- initial values for solver ---
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#self.opti.set_initial(speed, 1)
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#self.opti.set_initial(T, 1)
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tstart = time.time()
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x = SX.sym('x')
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y = SX.sym('y')
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theta = SX.sym('theta')
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state = vertcat(x, y, theta)
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r = 0.03
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R = 0.05
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d = 0.02
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omega_max = 13.32
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omegar = SX.sym('omegar')
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omegal = SX.sym('omegal')
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control = vertcat(omegar, omegal)
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# model equation
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f1 = (r / 2 * cos(theta) - r * d / (2 * R) * sin(theta)) * omegar * omega_max + (r / 2 * cos(theta) + r * d / (
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2 * R) * sin(
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theta)) * omegal * omega_max
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f2 = (r / 2 * sin(theta) + r * d / (2 * R) * cos(theta)) * omegar * omega_max + (r / 2 * sin(theta) - r * d / (
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2 * R) * cos(
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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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self.f = Function('f', [state, control], [xdot, L])
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# ---- solve NLP ------
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# set numerical backend
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# delete constraints
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self.opti.subject_to()
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# add new constraints
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dt = self.T / self.N # length of a control interval
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for k in range(self.N): # loop over control intervals
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# Runge-Kutta 4 integration
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k1, k1_q = self.f(self.X[:, k], self.U[:, k])
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k2, k2_q = self.f(self.X[:, k] + dt / 2 * k1, self.U[:, k])
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k3, k3_q = self.f(self.X[:, k] + dt / 2 * k2, self.U[:, k])
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k4, k4_q = self.f(self.X[:, k] + dt * k3, self.U[:, k])
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x_next = self.X[:, k] + dt / 6 * (k1 + 2 * k2 + 2 * k3 + k4)
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q_next = self.Q[:, k] + dt / 6 * (k1_q + 2 * k2_q + 2 * k3_q + k4_q)
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self.opti.subject_to(self.X[:, k + 1] == x_next) # close the gaps
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self.opti.subject_to(self.Q[:, k + 1] == q_next) # close the gaps
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self.opti.minimize(self.Q[:, self.N] + 1.0e5 * self.slack**2)
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# ---- path constraints -----------
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# limit = lambda pos: 1-sin(2*pi*pos)/2
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# self.opti.subject_to(speed<=limit(pos)) # track speed limit
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maxcontrol = 0.95
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self.opti.subject_to(self.opti.bounded(-maxcontrol, self.U, maxcontrol)) # control is limited
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# ---- boundary conditions --------
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# self.opti.subject_to(speed[-1]==0) # .. with speed 0
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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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# ---- misc. constraints ----------
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# self.opti.subject_to(X[1,:]>=0) # Time must be positive
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# self.opti.subject_to(X[2,:]<=4) # Time must be positive
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# self.opti.subject_to(X[2,:]>=-2) # Time must be positive
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# avoid obstacle
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for o in obstacles:
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p = obstacles[o].pos
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r = obstacles[o].radius
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if p is not None:
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for k in range(1,self.N):
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self.opti.subject_to((self.X[0,k]-p[0])**2 + (self.X[1,k]-p[1])**2 + self.slack > r**2)
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# pass
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posx = self.X[0, :]
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posy = self.X[1, :]
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angle = self.X[2, :]
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self.opti.subject_to(posx[0] == x0[0]) # start at position 0 ...
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self.opti.subject_to(posy[0] == x0[1]) # ... from stand-still
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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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tstart = time.time()
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if self.use_warmstart and self.opti_x0 is not None:
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self.opti.set_initial(self.opti.lam_g, self.opti_lam_g0)
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self.opti.set_initial(self.opti.x, self.opti_x0)
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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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self.opti_x0 = sol.value(self.opti.x)
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self.opti_lam_g0 = sol.value(self.opti.lam_g)
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#u_opt_1 = map(lambda x: float(x), [u_opt[i * 2] for i in range(0, 60)])
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#u_opt_2 = map(lambda x: float(x), [u_opt[i * 2 + 1] for i in range(0, 60)])
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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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return (u_opt_1, u_opt_2, sol.value(posx), sol.value(posy))
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#lam_g0 = sol.value(self.opti.lam_g)
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#self.opti.solve()
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from pylab import plot, step, figure, legend, show, spy
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plot(sol.value(posx),label="posx")
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plot(sol.value(posy),label="posy")
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plot(sol.value(angle),label="angle")
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plt.figure(3)
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plot(sol.value(posx), sol.value(posy))
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ax = plt.gca()
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#circle = plt.Circle(p, r)
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#ax.add_artist(circle)
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#plot(limit(sol.value(pos)),'r--',label="speed limit")
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#step(range(N),sol.value(U),'k',label="throttle")
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legend(loc="upper left")
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plt.show()
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pass
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# linearization
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# A = zeros(states.shape[0], states.shape[0])
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# for i in range(f.shape[0]):
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# for j in range(states.shape[0]):
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# A[i,j] = diff(f[i,0], states[j])
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# Alin = A.subs([(theta,0), (omegar,0), (omegal,0)])
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# print("A = {}".format(Alin))
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# B = zeros(f.shape[0], controls.shape[0])
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# for i in range(f.shape[0]):
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# for j in range(controls.shape[0]):
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# B[i,j] = diff(f[i,0], controls[j])
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# print("B = {}".format(B))
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# dfdtheta = diff(f, theta)
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#print(dfdtheta.doit())
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# takeaway: linearization is not helpful, because the linearized system is not stabilizable
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# -> alternative: use nonlinear control method
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