forked from Telos4/RoboRally
working version of MPC controller
This commit is contained in:
parent
f100f21162
commit
b8927cf1c5
|
|
@ -54,46 +54,64 @@ class Robot:
|
|||
print("connected!")
|
||||
listening = True
|
||||
while listening:
|
||||
# expected data: '(u1, u2)'\n"
|
||||
# expected data: '(t, u1, u2)'\n"
|
||||
# where ui = control for motor i
|
||||
# ui \in [-1.0, 1.0]
|
||||
try:
|
||||
data = comm_socket.readline()
|
||||
data_str = data.decode()
|
||||
#print("Data received: {}".format(data_str))
|
||||
#print("processing data = {}".format(data_str))
|
||||
print("Data received: {}".format(data_str))
|
||||
print("processing data = {}".format(data_str))
|
||||
l = data_str.strip('()\n').split(',')
|
||||
#print("l = {}".format(l))
|
||||
u1 = int(float(l[0])*100)
|
||||
#print("u1 = {}".format(u1))
|
||||
u2 = int(float(l[1])*100)
|
||||
#print("u2 = {}".format(u2))
|
||||
print("l = {}".format(l))
|
||||
t = float(l[0])
|
||||
print("t = {}".format(t))
|
||||
u1 = int(float(l[1])*100)
|
||||
print("u1 = {}".format(u1))
|
||||
u2 = int(float(l[2])*100)
|
||||
print("u2 = {}".format(u2))
|
||||
except ValueError:
|
||||
print("ValueError: Data has wrong format.")
|
||||
print("Data received: {}".format(data_str))
|
||||
print("Shutting down ...")
|
||||
u1 = u2 = 0
|
||||
t = 0.0
|
||||
listening = False
|
||||
except IndexError:
|
||||
print("IndexError: Data has wrong format.")
|
||||
print("Data received: {}".format(data_str))
|
||||
print("Shutting down ...")
|
||||
u1 = u2 = 0
|
||||
listening = False
|
||||
except Exception as e:
|
||||
print("Some other error occured")
|
||||
print("Exception: {}".format(e))
|
||||
print("Shutting down ...")
|
||||
u1 = u2 = 0
|
||||
listening = False
|
||||
finally:
|
||||
self.m1.speed(u1)
|
||||
self.m2.speed(u2)
|
||||
comm_socket.close()
|
||||
socket.close()
|
||||
del comm_socket
|
||||
del socket
|
||||
print("disconnected!")
|
||||
except IndexError:
|
||||
print("IndexError: Data has wrong format.")
|
||||
print("Data received: {}".format(data_str))
|
||||
print("Shutting down ...")
|
||||
u1 = u2 = 0
|
||||
t = 0.0
|
||||
listening = False
|
||||
comm_socket.close()
|
||||
socket.close()
|
||||
del comm_socket
|
||||
del socket
|
||||
print("disconnected!")
|
||||
except Exception as e:
|
||||
print("Some other error occured")
|
||||
print("Exception: {}".format(e))
|
||||
print("Shutting down ...")
|
||||
u1 = u2 = 0
|
||||
t = 0.0
|
||||
listening = False
|
||||
comm_socket.close()
|
||||
socket.close()
|
||||
del comm_socket
|
||||
del socket
|
||||
print("disconnected!")
|
||||
finally:
|
||||
self.m1.speed(u1)
|
||||
self.m2.speed(u2)
|
||||
time.sleep(t)
|
||||
self.m1.speed(0)
|
||||
self.m2.speed(0)
|
||||
|
||||
wall_e = Robot()
|
||||
wall_e.remote_control()
|
||||
|
|
|
|||
|
|
@ -1,13 +1,14 @@
|
|||
from casadi import *
|
||||
import time
|
||||
|
||||
# look at: https://github.com/casadi/casadi/blob/master/docs/examples/python/vdp_indirect_multiple_shooting.py
|
||||
|
||||
class OpenLoopSolver:
|
||||
def __init__(self, N=60, T=6.0):
|
||||
def __init__(self, N=20, T=2.0):
|
||||
self.T = T
|
||||
self.N = N
|
||||
|
||||
def solve(self, x0):
|
||||
def setup(self):
|
||||
x = SX.sym('x')
|
||||
y = SX.sym('y')
|
||||
theta = SX.sym('theta')
|
||||
|
|
@ -15,29 +16,31 @@ class OpenLoopSolver:
|
|||
r = 0.03
|
||||
R = 0.05
|
||||
d = 0.02
|
||||
omega_max = 13.32
|
||||
|
||||
omegar = SX.sym('omegar')
|
||||
omegal = SX.sym('omegal')
|
||||
control = vertcat(omegar, omegal)
|
||||
|
||||
# model equation
|
||||
f1 = (r / 2 * cos(theta) - r * d / (2 * R) * sin(theta)) * omegar + (r / 2 * cos(theta) + r * d / (2 * R) * sin(
|
||||
theta)) * omegal
|
||||
f2 = (r / 2 * sin(theta) + r * d / (2 * R) * cos(theta)) * omegar + (r / 2 * sin(theta) - r * d / (2 * R) * cos(
|
||||
theta)) * omegal
|
||||
f3 = r / (2 * R) * omegar - r / (2 * R) * omegal
|
||||
f1 = (r / 2 * cos(theta) - r * d / (2 * R) * sin(theta)) * omegar * omega_max + (r / 2 * cos(theta) + r * d / (2 * R) * sin(
|
||||
theta)) * omegal * omega_max
|
||||
f2 = (r / 2 * sin(theta) + r * d / (2 * R) * cos(theta)) * omegar * omega_max + (r / 2 * sin(theta) - r * d / (2 * R) * cos(
|
||||
theta)) * omegal * omega_max
|
||||
f3 = -(r / (2 * R) * omegar - r / (2 * R) * omegal) * omega_max
|
||||
xdot = vertcat(f1, f2, f3)
|
||||
f = Function('f', [x, y, theta, omegar, omegal], [f1, f2, f3])
|
||||
print("f = {}".format(f))
|
||||
|
||||
# cost functional
|
||||
L = x ** 2 + y ** 2 + 1e-2 * theta ** 2 + 1e-4 * (omegar ** 2 + omegal ** 2)
|
||||
target = (-0.0, 0.0)
|
||||
L = (x-target[0]) ** 2 + (y-target[1]) ** 2 + 1e-2 * theta ** 2 + 1e-2 * (omegar ** 2 + omegal ** 2)
|
||||
|
||||
# Fixed step Runge-Kutta 4 integrator
|
||||
M = 4 # RK4 steps per interval
|
||||
DT = self.T / self.N / M
|
||||
print("DT = {}".format(DT))
|
||||
f = Function('f', [state, control], [xdot, L])
|
||||
self.f = Function('f', [state, control], [xdot, L])
|
||||
X0 = MX.sym('X0', 3)
|
||||
U = MX.sym('U', 2)
|
||||
X = X0
|
||||
|
|
@ -45,10 +48,10 @@ class OpenLoopSolver:
|
|||
runge_kutta = True
|
||||
if runge_kutta:
|
||||
for j in range(M):
|
||||
k1, k1_q = f(X, U)
|
||||
k2, k2_q = f(X + DT / 2 * k1, U)
|
||||
k3, k3_q = f(X + DT / 2 * k2, U)
|
||||
k4, k4_q = f(X + DT * k3, U)
|
||||
k1, k1_q = self.f(X, U)
|
||||
k2, k2_q = self.f(X + DT / 2 * k1, U)
|
||||
k3, k3_q = self.f(X + DT / 2 * k2, U)
|
||||
k4, k4_q = self.f(X + DT * k3, U)
|
||||
X = X + DT / 6 * (k1 + 2 * k2 + 2 * k3 + k4)
|
||||
Q = Q + DT / 6 * (k1_q + 2 * k2_q + 2 * k3_q + k4_q)
|
||||
else:
|
||||
|
|
@ -75,37 +78,38 @@ class OpenLoopSolver:
|
|||
ubg = []
|
||||
|
||||
# Formulate the NLP
|
||||
Xk = MX(x0)
|
||||
for k in range(self.N):
|
||||
# New NLP variable for the control
|
||||
U1k = MX.sym('U1_' + str(k), 2)
|
||||
# U2k = MX.sym('U2_' + str(k))
|
||||
w += [U1k]
|
||||
lbw += [-10, -10]
|
||||
ubw += [10, 10]
|
||||
w0 += [0, 0]
|
||||
|
||||
# Integrate till the end of the interval
|
||||
Fk = F(x0=Xk, p=U1k)
|
||||
Xk = Fk['xf']
|
||||
J = J + Fk['qf']
|
||||
|
||||
# Add inequality constraint
|
||||
# g += [Xk[1]]
|
||||
# lbg += [-.0]
|
||||
# ubg += [inf]
|
||||
|
||||
# Create an NLP solver
|
||||
prob = {'f': J, 'x': vertcat(*w), 'g': vertcat(*g)}
|
||||
self.solver = nlpsol('solver', 'ipopt', prob)
|
||||
# Xk = MX(x0)
|
||||
# for k in range(self.N):
|
||||
# # New NLP variable for the control
|
||||
# U1k = MX.sym('U1_' + str(k), 2)
|
||||
# # U2k = MX.sym('U2_' + str(k))
|
||||
# w += [U1k]
|
||||
# lbw += [-0.5, -0.5]
|
||||
# ubw += [0.5, 0.5]
|
||||
# w0 += [0, 0]
|
||||
#
|
||||
# # Integrate till the end of the interval
|
||||
# Fk = F(x0=Xk, p=U1k)
|
||||
# Xk = Fk['xf']
|
||||
# J = J + Fk['qf']
|
||||
#
|
||||
# # Add inequality constraint
|
||||
# # g += [Xk[1]]
|
||||
# # lbg += [-.0]
|
||||
# # ubg += [inf]
|
||||
#
|
||||
# # Create an NLP solver
|
||||
# prob = {'f': J, 'x': vertcat(*w), 'g': vertcat(*g)}
|
||||
# self.solver = nlpsol('solver', 'ipopt', prob)
|
||||
|
||||
# Solve the NLP
|
||||
if False:
|
||||
sol = self.solver(x0=w0, lbx=lbw, ubx=ubw, lbg=lbg, ubg=ubg)
|
||||
w_opt = sol['x']
|
||||
|
||||
# Plot the solution
|
||||
u_opt = w_opt
|
||||
x_opt = [x0]
|
||||
x_opt = [self.x0]
|
||||
for k in range(self.N):
|
||||
Fk = F(x0=x_opt[-1], p=u_opt[2*k:2*k+2])
|
||||
x_opt += [Fk['xf'].full()]
|
||||
|
|
@ -114,88 +118,140 @@ class OpenLoopSolver:
|
|||
x3_opt = [r[2] for r in x_opt]
|
||||
|
||||
tgrid = [self.T/self.N*k for k in range(self.N+1)]
|
||||
import matplotlib.pyplot as plt
|
||||
plt.figure(2)
|
||||
plt.clf()
|
||||
plt.plot(tgrid, x1_opt, '--')
|
||||
plt.plot(tgrid, x2_opt, '-')
|
||||
plt.plot(tgrid, x3_opt, '*')
|
||||
#import matplotlib.pyplot as plt
|
||||
#plt.figure(2)
|
||||
#plt.clf()
|
||||
#plt.plot(tgrid, x1_opt, '--')
|
||||
#plt.plot(tgrid, x2_opt, '-')
|
||||
#plt.plot(tgrid, x3_opt, '*')
|
||||
#plt.step(tgrid, vertcat(DM.nan(1), u_opt), '-.')
|
||||
plt.xlabel('t')
|
||||
plt.legend(['x1','x2','x3','u'])
|
||||
plt.grid()
|
||||
#plt.xlabel('t')
|
||||
#plt.legend(['x1','x2','x3','u'])
|
||||
#plt.grid()
|
||||
#plt.show()
|
||||
#return
|
||||
|
||||
# alternative solution using multiple shooting (way faster!)
|
||||
opti = Opti() # Optimization problem
|
||||
self.opti = Opti() # Optimization problem
|
||||
|
||||
# ---- decision variables ---------
|
||||
X = opti.variable(3,self.N+1) # state trajectory
|
||||
Q = opti.variable(1,self.N+1) # state trajectory
|
||||
posx = X[0,:]
|
||||
posy = X[1,:]
|
||||
angle = X[2,:]
|
||||
U = opti.variable(2,self.N) # control trajectory (throttle)
|
||||
#T = opti.variable() # final time
|
||||
self.X = self.opti.variable(3,self.N+1) # state trajectory
|
||||
self.Q = self.opti.variable(1,self.N+1) # state trajectory
|
||||
|
||||
self.U = self.opti.variable(2,self.N) # control trajectory (throttle)
|
||||
#T = self.opti.variable() # final time
|
||||
|
||||
# ---- objective ---------
|
||||
#opti.minimize(T) # race in minimal time
|
||||
#self.opti.minimize(T) # race in minimal time
|
||||
|
||||
# ---- dynamic constraints --------
|
||||
#f = lambda x,u: vertcat(f1, f2, f3) # dx/dt = f(x,u)
|
||||
|
||||
|
||||
|
||||
# ---- initial values for solver ---
|
||||
#self.opti.set_initial(speed, 1)
|
||||
#self.opti.set_initial(T, 1)
|
||||
|
||||
|
||||
def solve(self, x0, target):
|
||||
|
||||
tstart = time.time()
|
||||
x = SX.sym('x')
|
||||
y = SX.sym('y')
|
||||
theta = SX.sym('theta')
|
||||
state = vertcat(x, y, theta)
|
||||
r = 0.03
|
||||
R = 0.05
|
||||
d = 0.02
|
||||
omega_max = 13.32
|
||||
|
||||
omegar = SX.sym('omegar')
|
||||
omegal = SX.sym('omegal')
|
||||
control = vertcat(omegar, omegal)
|
||||
|
||||
# model equation
|
||||
f1 = (r / 2 * cos(theta) - r * d / (2 * R) * sin(theta)) * omegar * omega_max + (r / 2 * cos(theta) + r * d / (
|
||||
2 * R) * sin(
|
||||
theta)) * omegal * omega_max
|
||||
f2 = (r / 2 * sin(theta) + r * d / (2 * R) * cos(theta)) * omegar * omega_max + (r / 2 * sin(theta) - r * d / (
|
||||
2 * R) * cos(
|
||||
theta)) * omegal * omega_max
|
||||
f3 = -(r / (2 * R) * omegar - r / (2 * R) * omegal) * omega_max
|
||||
xdot = vertcat(f1, f2, f3)
|
||||
L = (x - target[0]) ** 2 + (y - target[1]) ** 2 + 1e-2 * theta ** 2 + 1e-2 * (omegar ** 2 + omegal ** 2)
|
||||
self.f = Function('f', [state, control], [xdot, L])
|
||||
# ---- solve NLP ------
|
||||
|
||||
# set numerical backend
|
||||
|
||||
|
||||
# delete constraints
|
||||
self.opti.subject_to()
|
||||
|
||||
# add new constraints
|
||||
dt = self.T / self.N # length of a control interval
|
||||
for k in range(self.N): # loop over control intervals
|
||||
# Runge-Kutta 4 integration
|
||||
k1, k1_q = f(X[:,k], U[:,k])
|
||||
k2, k2_q = f(X[:,k]+dt/2*k1, U[:,k])
|
||||
k3, k3_q = f(X[:,k]+dt/2*k2, U[:,k])
|
||||
k4, k4_q = f(X[:,k]+dt*k3, U[:,k])
|
||||
x_next = X[:,k] + dt/6*(k1+2*k2+2*k3+k4)
|
||||
q_next = Q[:,k] + dt/6*(k1_q + 2 * k2_q + 2 * k3_q + k4_q)
|
||||
opti.subject_to(X[:,k+1]==x_next) # close the gaps
|
||||
opti.subject_to(Q[:,k+1]==q_next) # close the gaps
|
||||
opti.minimize(Q[:,self.N])
|
||||
k1, k1_q = self.f(self.X[:, k], self.U[:, k])
|
||||
k2, k2_q = self.f(self.X[:, k] + dt / 2 * k1, self.U[:, k])
|
||||
k3, k3_q = self.f(self.X[:, k] + dt / 2 * k2, self.U[:, k])
|
||||
k4, k4_q = self.f(self.X[:, k] + dt * k3, self.U[:, k])
|
||||
x_next = self.X[:, k] + dt / 6 * (k1 + 2 * k2 + 2 * k3 + k4)
|
||||
q_next = self.Q[:, k] + dt / 6 * (k1_q + 2 * k2_q + 2 * k3_q + k4_q)
|
||||
self.opti.subject_to(self.X[:, k + 1] == x_next) # close the gaps
|
||||
self.opti.subject_to(self.Q[:, k + 1] == q_next) # close the gaps
|
||||
self.opti.minimize(self.Q[:, self.N])
|
||||
|
||||
# ---- path constraints -----------
|
||||
# limit = lambda pos: 1-sin(2*pi*pos)/2
|
||||
#opti.subject_to(speed<=limit(pos)) # track speed limit
|
||||
opti.subject_to(opti.bounded(-10,U,10)) # control is limited
|
||||
# self.opti.subject_to(speed<=limit(pos)) # track speed limit
|
||||
self.opti.subject_to(self.opti.bounded(-0.5, self.U, 0.5)) # control is limited
|
||||
|
||||
# ---- boundary conditions --------
|
||||
opti.subject_to(posx[0]==x0[0]) # start at position 0 ...
|
||||
opti.subject_to(posy[0]==x0[1]) # ... from stand-still
|
||||
opti.subject_to(angle[0]==x0[2]) # finish line at position 1
|
||||
#opti.subject_to(speed[-1]==0) # .. with speed 0
|
||||
opti.subject_to(Q[:,0]==0.0)
|
||||
|
||||
# self.opti.subject_to(speed[-1]==0) # .. with speed 0
|
||||
self.opti.subject_to(self.Q[:, 0] == 0.0)
|
||||
|
||||
self.opti.solver("ipopt")
|
||||
|
||||
# ---- misc. constraints ----------
|
||||
#opti.subject_to(X[1,:]>=0) # Time must be positive
|
||||
#opti.subject_to(X[2,:]<=4) # Time must be positive
|
||||
#opti.subject_to(X[2,:]>=-2) # Time must be positive
|
||||
# self.opti.subject_to(X[1,:]>=0) # Time must be positive
|
||||
# self.opti.subject_to(X[2,:]<=4) # Time must be positive
|
||||
# self.opti.subject_to(X[2,:]>=-2) # Time must be positive
|
||||
|
||||
# avoid obstacle
|
||||
# r = 0.25
|
||||
# p = (0.5, 0.5)
|
||||
# for k in range(self.N):
|
||||
# opti.subject_to((X[0,k]-p[0])**2 + (X[1,k]-p[1])**2 > r**2)
|
||||
# self.opti.subject_to((X[0,k]-p[0])**2 + (X[1,k]-p[1])**2 > r**2)
|
||||
# pass
|
||||
posx = self.X[0, :]
|
||||
posy = self.X[1, :]
|
||||
angle = self.X[2, :]
|
||||
|
||||
self.opti.subject_to(posx[0] == x0[0]) # start at position 0 ...
|
||||
self.opti.subject_to(posy[0] == x0[1]) # ... from stand-still
|
||||
self.opti.subject_to(angle[0] == x0[2]) # finish line at position 1
|
||||
tend = time.time()
|
||||
|
||||
# ---- initial values for solver ---
|
||||
#opti.set_initial(speed, 1)
|
||||
#opti.set_initial(T, 1)
|
||||
print("setting up problem took {} seconds".format(tend - tstart))
|
||||
|
||||
# ---- solve NLP ------
|
||||
opti.solver("ipopt") # set numerical backend
|
||||
sol = opti.solve() # actual solve
|
||||
sol = self.opti.solve() # actual solve
|
||||
|
||||
#x0 = sol.value(opti.x)
|
||||
#lam_g0 = sol.value(opti.lam_g)
|
||||
#opti.set_initial(opti.lam_g, lam_g0)
|
||||
#opti.set_initial(opti.x, x0)
|
||||
#opti.solve()
|
||||
#x0 = sol.value(self.opti.x)
|
||||
|
||||
#u_opt_1 = map(lambda x: float(x), [u_opt[i * 2] for i in range(0, 60)])
|
||||
#u_opt_2 = map(lambda x: float(x), [u_opt[i * 2 + 1] for i in range(0, 60)])
|
||||
u_opt_1 = sol.value(self.U[0,:])
|
||||
u_opt_2 = sol.value(self.U[1,:])
|
||||
|
||||
return (u_opt_1, u_opt_2)
|
||||
|
||||
#lam_g0 = sol.value(self.opti.lam_g)
|
||||
#self.opti.set_initial(self.opti.lam_g, lam_g0)
|
||||
#self.opti.set_initial(self.opti.x, x0)
|
||||
#self.opti.solve()
|
||||
|
||||
from pylab import plot, step, figure, legend, show, spy
|
||||
|
||||
|
|
@ -206,8 +262,8 @@ class OpenLoopSolver:
|
|||
plt.figure(3)
|
||||
plot(sol.value(posx), sol.value(posy))
|
||||
ax = plt.gca()
|
||||
circle = plt.Circle(p, r)
|
||||
ax.add_artist(circle)
|
||||
#circle = plt.Circle(p, r)
|
||||
#ax.add_artist(circle)
|
||||
#plot(limit(sol.value(pos)),'r--',label="speed limit")
|
||||
#step(range(N),sol.value(U),'k',label="throttle")
|
||||
legend(loc="upper left")
|
||||
|
|
|
|||
|
|
@ -11,6 +11,7 @@ import pygame
|
|||
import numpy as np
|
||||
import socket
|
||||
import scipy.integrate
|
||||
import copy
|
||||
|
||||
import threading
|
||||
from copy import deepcopy
|
||||
|
|
@ -118,8 +119,12 @@ class RemoteController:
|
|||
self.dirs, = self.ax.plot([], [])
|
||||
plt.xlabel('x-position')
|
||||
plt.ylabel('y-position')
|
||||
plt.grid()
|
||||
|
||||
self.ols = OpenLoopSolver()
|
||||
self.ols.setup()
|
||||
|
||||
self.target = (0.0, 0.0)
|
||||
|
||||
def ani(self):
|
||||
self.ani = anim.FuncAnimation(self.fig, init_func=self.ani_init, func=self.ani_update, interval=10, blit=True)
|
||||
|
|
@ -207,6 +212,9 @@ class RemoteController:
|
|||
self.mutex.release()
|
||||
|
||||
def controller(self):
|
||||
tgrid = None
|
||||
us1 = None
|
||||
us2 = None
|
||||
print("starting control")
|
||||
while True:
|
||||
|
||||
|
|
@ -342,7 +350,66 @@ class RemoteController:
|
|||
for event in events:
|
||||
if event.type == pygame.KEYDOWN:
|
||||
if event.key == pygame.K_UP:
|
||||
self.ols.solve(self.xms[-1])
|
||||
self.controlling = True
|
||||
elif event.key == pygame.K_DOWN:
|
||||
self.controlling = False
|
||||
self.rc_socket.send('(0.0,0.0)\n')
|
||||
elif event.key == pygame.K_0:
|
||||
self.target = (0.0, 0.0)
|
||||
elif event.key == pygame.K_1:
|
||||
self.target = (0.5,0.5)
|
||||
elif event.key == pygame.K_2:
|
||||
self.target = (0.5, -0.5)
|
||||
elif event.key == pygame.K_3:
|
||||
self.target = (-0.5,-0.5)
|
||||
elif event.key == pygame.K_4:
|
||||
self.target = (-0.5,0.5)
|
||||
if self.controlling:
|
||||
tmpc_start = time.time()
|
||||
# get measurement
|
||||
last_measurement = copy.deepcopy(self.xms[-1])
|
||||
res = self.ols.solve(last_measurement, self.target)
|
||||
#tgrid = res[0]
|
||||
us1 = res[0]
|
||||
us2 = res[1]
|
||||
|
||||
# tt = 0
|
||||
# x = last_measurement
|
||||
# t_ol = np.array([tt])
|
||||
# x_ol = np.array([x])
|
||||
# # compute open loop prediction
|
||||
# for i in range(len(us1)):
|
||||
# r = scipy.integrate.ode(f_ode)
|
||||
# r.set_f_params(np.array([us1[i] * 13.32, us2[i] * 13.32]))
|
||||
# r.set_initial_value(x, tt)
|
||||
#
|
||||
# tt = tt + 0.1
|
||||
# x = r.integrate(tt)
|
||||
#
|
||||
# t_ol = np.vstack((t_ol, tt))
|
||||
# x_ol = np.vstack((x_ol, x))
|
||||
|
||||
#plt.figure(4)
|
||||
#plt.plot(x_ol[:,0], x_ol[:,1])
|
||||
|
||||
|
||||
#if event.key == pygame.K_DOWN:
|
||||
# if tgrid is not None:
|
||||
tmpc_end = time.time()
|
||||
print("---------------- mpc solution took {} seconds".format(tmpc_end-tmpc_start))
|
||||
for i in range(0, 1):
|
||||
u1 = us1[i]
|
||||
u2 = us2[i]
|
||||
dt_mpc = time.time() - self.t
|
||||
#if dt_mpc < 0.1:
|
||||
# time.sleep(0.1 - dt_mpc)
|
||||
self.rc_socket.send('({},{},{})\n'.format(0.1,u1, u2))
|
||||
self.t = time.time()
|
||||
time.sleep(0.01)
|
||||
#
|
||||
|
||||
|
||||
pass
|
||||
|
||||
def main(args):
|
||||
rospy.init_node('controller_node', anonymous=True)
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user