def solve(x_0, covMatrix, bounds):
global covMatrix_, n_grad
n_grad = 0
covMatrix_ = covMatrix
inf = 1.0e20
n = len(x_0)
snopt = SNOPT_solver()
snopt.setOption("Verbose", False)
snopt.setOption("Specs file","/home/federico/workspace/TestMosek/algorithms/specs.spec")
xlow = np.array([bounds[0]]*n)
xupp = np.array([bounds[1]]*n)
Flow = np.array([0.0, 1.0])
Fupp = np.array([inf, 1.0])
ObjRow = 1
A = np.array([ [0]*n,
[1]*n
])
G = np.array([ [1]*n,
[0]*n
])
[exe_time, iterations] = snopt.snopta(x0=x_0,xlow=xlow,xupp=xupp,
Flow=Flow,Fupp=Fupp, ObjRow=ObjRow, A=A, G=G,
usrfun=objFG)
return exe_time, snopt.x, iterations
xp = xp.values
[xm, ym] = inter_min(xp, inter_par)
return xm, ym, cse
# p=interpolate_val(x,inter_par);
# e=R2-(x-xc)'*(x-xc);
# M=-e/(p-y0);
# if p<y0
# M=-inf;
# end
inf = 1.0e20
snopt = SNOPT_solver()
snopt.setOption('Verbose', True)
snopt.setOption('Solution print', True)
snopt.setOption('Print file', 'sntoya.out')
# Either dtype works, but the names for x and F have to be of
# the correct length, else they are both ignored by SNOPT:
xNames = np.array([' x0', ' x1'])
FNames = np.array([' F0', ' F1', ' F2'], dtype='c')
x0 = np.array([1.0, 1.0])
xlow = np.array([0.0, -inf])
xupp = np.array([inf, inf])
def optimize_blending_function_between_two_distance_sigmas(sigmaA, sigmaB, personA, personB, min_distA, min_distB, params, constrain_at_endpoints=False):
'''
This function finds a blend between two trajectories that respect two minimum distance constraints.
'''
# Some important parameters here
nsamples = params['nsamples'] if 'nsamples' in params else 50
ndims = 6
dt = 0.01
xdot5_limit = 0.001
inf = 1.0e20
lambda_snap = 1#(1/dt)**4 # snap must be scaled down to be comparable to position.
lambda_pos = 1
# A few derived quantities
nvars = ndims*nsamples
nconstraints_continuity = (ndims-1)*nsamples
nconstraints_obstacles = 2*nsamples
nconstraints = 1 + nconstraints_continuity + nconstraints_obstacles
# Solver configuration
snopt = SNOPT_solver()
snopt.setOption('Verbose',False)
snopt.setOption('Solution print',False)
snopt.setOption('Print file','test5.out')
snopt.setOption('Iteration limit',8000)
snopt.setOption('Print level',3)
snopt.setOption('Major optimality',2e-6)
snopt.setOption('Verify level',3) # Turn to 3 to carefully check gradiants
# 1. Set up decision variables
x = np.array([0.5]*nsamples) # Initialize to 0.5
xdot1 = np.array([0.0]*nsamples)
xdot2 = np.array([0.0]*nsamples)
xdot3 = np.array([0.0]*nsamples)
xdot4 = np.array([0.0]*nsamples)
v = np.array([0.0]*nsamples) # C4 Continuity Control Variable
x0 = np.matrix(np.c_[x,xdot1,xdot2,xdot3,xdot4,v]).A1 # Interleave [x[0],xdot1[0],xdot2[0]...]
# 2. Set up the bounds on x
low_x = np.array([ 0.0] *nsamples) # X must be greater or equal to 0
low_xdot1 = np.array([ -inf]*nsamples)
low_xdot2 = np.array([ -inf]*nsamples)
low_xdot3 = np.array([ -inf]*nsamples)
low_xdot4 = np.array([ -inf]*nsamples)
low_v = np.array([ -xdot5_limit]*nsamples) # Bound control variable arbitrarily
if constrain_at_endpoints:
low_x[0] = 0.5
low_x[nsamples-1] = 0.5
xlow = np.matrix(np.c_[low_x,low_xdot1,low_xdot2,low_xdot3,low_xdot4,low_v]).A1 # Interleave [x[0],xdot1[0],xdot2[0]...]
upp_x = np.array([ 1.0] *nsamples) # X must be greater or equal to 0
upp_xdot1 = np.array([ inf]*nsamples)
upp_xdot2 = np.array([ inf]*nsamples)
upp_xdot3 = np.array([ inf]*nsamples)
upp_xdot4 = np.array([ inf]*nsamples)
upp_v = np.array([ xdot5_limit]*nsamples) # Bound control variable arbitrarily
if constrain_at_endpoints:
upp_x[0] = 0.5
upp_x[nsamples-1] = 0.5
xupp = np.matrix(np.c_[upp_x,upp_xdot1,upp_xdot2,upp_xdot3,upp_xdot4,upp_v]).A1 # Interleave [x[0],xdot1[0],xdot2[0]...]
# 3. Set up the objective function
M = np.array([
[0,1,0,0,0],
[0,0,1,0,0],
[0,0,0,1,0],
[0,0,0,0,1],
[0,0,0,0,0]
])
N = np.array([0,0,0,0,1])
def grad_function(xM, compute_nonzero_only=False, compute_linear=False):
G = np.zeros((nconstraints, nvars))
# Set up the jacobian structure of the cost function.
# This only impacts the w_i and wdot4_i variables
obj_col = G[0,:]
if not compute_nonzero_only:
obj_col[::6] = 2*dt*lambda_pos*(xM[:,0] - 0.5)
obj_col[4::6] = 2*dt*lambda_snap*xM[:,4]
elif not compute_linear:
obj_col[::6] = 1
obj_col[4::6] = 1
if compute_linear:
# The C4 continuity constraint is linear
stupidcounter = 0
add_to_fi = 0
for fi in range(1,nconstraints_continuity-5): # Looping over the objective function
fi_row = G[fi,:]
fi += add_to_fi
fi_row[fi-1] = 1
fi_row[fi] = dt
fi_row[fi+5] = -1
stupidcounter += 1
if stupidcounter == 5:
add_to_fi += 1
stupidcounter = 0
return G
def calc_obj(xM):
# our objective is the sum of
# the L2 norm of our position error away from 0.5
# the L2 norm of our 4th derivative error away from 0
obj_pos = dt * np.sum( (xM[:,0] - 0.5)**2)
obj_snap = dt * np.sum( (xM[:,4] )**2)
objective = lambda_pos * obj_pos + lambda_snap * obj_snap
return (objective, obj_pos, obj_snap)
def calc_obstacle_constraints(xM):
blend = xM[:,0]
sigmaBlended = (blend[:,np.newaxis]*sigmaA + (1-blend)[:,np.newaxis]*sigmaB)
constraintA = la.norm(sigmaBlended - personA, axis=1) - min_distA
constraintB = la.norm(sigmaBlended - personB, axis=1) - min_distB
return np.r_[constraintA, constraintB]
def blend_test3_objFG(status,x,needF,needG,cu,iu,ru):
xM = x.reshape(nsamples,ndims)
objective, obj_pos, obj_snap = calc_obj(xM)
# Evaluate the current continuity constraints
continuity_x = np.zeros((nsamples, 5))
for i in range(nsamples-1):
si = xM[i,0:5]
vi = xM[i,5 ]
si1 = xM[i+1,0:5]
continuity_x[i] = si + (M.dot(si) + N.dot(vi))*dt - si1
continuity_x = np.matrix(continuity_x).A1
obstacles = calc_obstacle_constraints(xM)
F = np.concatenate(
([objective],
continuity_x,
obstacles))
#G = grad_function(xM)
return status, F#, G[G_nonzero_inds]
# 4. Set up bounds on F
# [ objectivec can be anything, equal-to-zero for continuity, greater-than-0 for obstacles along traj]
low_F = np.concatenate(([-inf], np.array([0,0,0,0,0]*nsamples), [0 , 0]*nsamples))
upp_F = np.concatenate(([ inf], np.array([0,0,0,0,0]*nsamples), [inf, inf]*nsamples))
# Matrix uses fortran numbering or something
ObjRow = 1
# Set up the linear and nonlinear structure of the jacobian matrix
xM = x0.reshape(nsamples,ndims)
G = grad_function(xM,compute_nonzero_only=True, compute_linear=False)
G_nonzero_inds = G.nonzero()
A = grad_function(xM,compute_nonzero_only=True, compute_linear=True)
# Now we solve
a = time.time()
snopt.snopta(name='blend_test3',usrfun=blend_test3_objFG,x0=x0,xlow=xlow,xupp=xupp,
Flow=low_F,Fupp=upp_F,ObjRow=ObjRow)
b = time.time()
print "Solved in %.4fs" % (b - a)
print "Value of objective function: %.8f" % snopt.F[0]
print " lambda_pos: %f, lambda_snap: %f, " % (lambda_pos, lambda_snap)
print " objective: %f, obj_pos: %f, obj_snap: %f" % calc_obj(xM)
xM = snopt.x.reshape(nsamples, ndims)
return (xM, snopt)
# Nonlinear objective term only
fObj = 0.0
if mode == 0 or mode == 2:
fObj = sum**2
gObj = np.zeros(nnObj,float)
if mode == 1 or mode == 2:
gObj[0] = 2.0*sum
gObj[1] = 2.0*sum
gObj[2] = 2.0*sum
return mode, fObj, gObj
snoptb = SNOPT_solver()
inf = 1.0e+20
snoptb.setOption('Infinite bound',inf)
snoptb.setOption('Print file','sntoyb.out')
m = 4
n = 4
nnCon = 2
nnJac = 2
nnObj = 3
# J contains the sparsity pattern of the Jacobian matrix.
# For nonlinear elements, enter any nonzero number (in this case 100).
# Linear elements must be correctly defined.
def optimize(p_eval,psi_eval, \
t_nominal,user_progress_nominal,dt_nominal, \
const_vals_ti, \
x_min_ti,x_max_ti, \
u_min_ti,u_max_ti):
assert allclose(psi_eval, 0.0)
print "flashlight.trajectory_optimization.quadrotor3d_direct_transcription_nonconst_dt: Initializing optimization problem..."
sys_time_begin = time.time()
solver_time_begin = sys_time_begin
#
# find numerically stable and feasible trajectories to initialize the solver
#
numerically_stable_infeasible_trajectory = quadrotor3d_gaussian_time_stretch.optimize_numerically_stable_infeasible( p_eval,psi_eval, \
t_nominal,user_progress_nominal,dt_nominal, \
x_min_ti,x_max_ti, \
u_min_ti,u_max_ti, \
max_stretch_iters_numerically_stable, \
gauss_width_in_terms_of_dt_numerically_stable, \
gauss_max_in_terms_of_dt_numerically_stable, \
0 )
x_numerically_stable, u_numerically_stable, t_numerically_stable, user_progress_numerically_stable, dt_numerically_stable = numerically_stable_infeasible_trajectory
if use_gaussian_time_stretching_for_feasible:
# use gaussian time stretching to find a feasible trajectory
feasible_trajectory = quadrotor3d_gaussian_time_stretch.optimize_feasible( p_eval,psi_eval, \
t_numerically_stable,user_progress_numerically_stable,dt_numerically_stable, \
x_min_ti,x_max_ti, \
u_min_ti,u_max_ti, \
max_stretch_iters_feasible, \
gauss_width_in_terms_of_dt_feasible, \
gauss_max_in_terms_of_dt_feasible, \
extra_iters_feasible )
x_feasible, u_feasible, t_feasible, user_progress_feasible, dt_feasible = feasible_trajectory
else:
# use uniform time stretching to find a feasible trajectory
p_nominal, _, _, _ = curveutils.reparameterize_curve(
p_eval, user_progress_nominal)
psi_nominal, _, _, _ = curveutils.reparameterize_curve(
psi_eval, user_progress_nominal)
feasible_trajectory = quadrotor3d_uniform_time_stretch.optimize_feasible( p_nominal,psi_nominal,dt_nominal, \
x_min_ti,x_max_ti, \
u_min_ti,u_max_ti, \
max_bin_search_iters_feasible, \
dt_upper_init_feasible )
x_feasible, u_feasible, dt_scale_feasible = feasible_trajectory
t_feasible = t_nominal * dt_scale_feasible * dt_scale_extra_stretch_feasible
user_progress_feasible = user_progress_nominal
dt_feasible = dt_nominal * dt_scale_feasible * dt_scale_extra_stretch_feasible
# return user_progress_numerically_stable,None,None,None,None,t_numerically_stable,t_numerically_stable[-1]
# return user_progress_feasible,None,None,None,None,t_feasible,t_feasible[-1]
sys_time_end = time.time()
print "flashlight.optimize.quadrotor3d_fixed_path: Finished initializing optimization problem (%.03f seconds)." % (
sys_time_end - sys_time_begin)
#
# set up optimization problem constants
#
num_trajectory_samples = p_eval.shape[0]
num_x_dims = quadrotor3d.num_x_dims
num_u_dims = quadrotor3d.num_u_dims
num_dt_dims = 1
num_alpha_dims = num_x_dims + num_u_dims + num_dt_dims
x_p_inds = arange(0, 3)
x_e_inds = arange(3, 6)
num_x_p_inds = x_p_inds.size
# soft control effort constraints
lamb_J_control_effort = 0.0 * ones(num_trajectory_samples)
# soft position waypoint constraints
num_dims_J_x_p_waypoint_ref_ti = 3
lamb_J_x_p_waypoint = 0.01 * ones(num_trajectory_samples)
J_x_p_waypoint_ref = x_numerically_stable[:, 0:3]
# soft dt constraints
num_dims_J_dt_ref_ti = 1
lamb_J_dt = 0.0001 * ones(num_trajectory_samples)
J_dt_ref = dt_numerically_stable * ones(num_trajectory_samples)
# hard dynamics constraints
num_dims_g_dynamics_ti = num_x_dims
# hard state space waypoint constraints
num_dims_g_x_waypoint_ti = num_x_dims
num_dims_x_waypoint_ref_ti = num_x_dims
lamb_g_x_waypoint = zeros(num_trajectory_samples)
lamb_g_x_waypoint[[0, -1]] = 1
X_waypoint_ref = zeros(
(num_trajectory_samples, num_dims_x_waypoint_ref_ti))
X_waypoint_ref[0] = array([
p_eval[0, 0], p_eval[0, 1], p_eval[0, 2], 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0
])
X_waypoint_ref[-1] = array([
p_eval[-1, 0], p_eval[-1, 1], p_eval[-1, 2], 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0
])
lamb_g_x_waypoint_nonzero = nonzero(lamb_g_x_waypoint)[0]
num_lamb_g_x_waypoint_nonzero = len(lamb_g_x_waypoint_nonzero)
lamb_g_x_waypoint_ti_to_ti_sparse = -1 * ones_like(lamb_g_x_waypoint,
dtype=int32)
lamb_g_x_waypoint_ti_to_ti_sparse[lamb_g_x_waypoint_nonzero] = arange(
num_lamb_g_x_waypoint_nonzero)
# hard position waypoint constraints
num_dims_g_x_p_waypoint_ti = num_x_p_inds
num_dims_x_p_waypoint_ref_ti = num_x_p_inds
lamb_g_x_p_waypoint = zeros(num_trajectory_samples)
X_p_waypoint_ref = zeros(
(num_trajectory_samples, num_dims_x_p_waypoint_ref_ti))
lamb_g_x_p_waypoint_nonzero = nonzero(lamb_g_x_p_waypoint)[0]
num_lamb_g_x_p_waypoint_nonzero = len(lamb_g_x_p_waypoint_nonzero)
lamb_g_x_p_waypoint_ti_to_ti_sparse = -1 * ones_like(lamb_g_x_p_waypoint,
dtype=int32)
lamb_g_x_p_waypoint_ti_to_ti_sparse[lamb_g_x_p_waypoint_nonzero] = arange(
num_lamb_g_x_p_waypoint_nonzero)
# hard dt constraints
num_dims_g_dt_ti = 1
num_dims_dt_ref_ti = 1
lamb_g_dt = zeros(num_trajectory_samples)
dt_ref = zeros(num_trajectory_samples)
lamb_g_dt_nonzero = nonzero(lamb_g_dt)[0]
num_lamb_g_dt_nonzero = len(lamb_g_dt_nonzero)
lamb_g_dt_ti_to_ti_sparse = -1 * ones_like(lamb_g_dt, dtype=int32)
lamb_g_dt_ti_to_ti_sparse[lamb_g_dt_nonzero] = arange(
num_lamb_g_dt_nonzero)
dt_min_ti = dt_numerically_stable * 0.45
dt_max_ti = dt_feasible * 1.55
# stack all the const, lamb, and ref values
const_vals = tile(const_vals_ti, (num_trajectory_samples, 1))
lamb_vals = c_[lamb_J_control_effort, lamb_J_x_p_waypoint, lamb_J_dt,
lamb_g_x_waypoint, lamb_g_x_p_waypoint, lamb_g_dt]
ref_vals = c_[J_x_p_waypoint_ref, J_dt_ref, X_waypoint_ref,
X_p_waypoint_ref, dt_ref]
# number of constraints and decision variables
num_constraints_g_dynamics = num_trajectory_samples - 1
num_dims_g_dynamics_ti = num_x_dims
num_constraints_1d_g_dynamics = num_constraints_g_dynamics * num_dims_g_dynamics_ti
num_constraints_1d_g_x_waypoint = num_lamb_g_x_waypoint_nonzero * num_dims_g_x_waypoint_ti
num_constraints_1d_g_x_p_waypoint = num_lamb_g_x_p_waypoint_nonzero * num_dims_g_x_p_waypoint_ti
num_constraints_1d_g_dt = num_lamb_g_dt_nonzero * num_dims_g_dt_ti
num_decision_vars_1d_X = num_trajectory_samples * num_x_dims
num_decision_vars_1d_U = num_trajectory_samples * num_u_dims
num_decision_vars_1d_DT = num_trajectory_samples * num_dt_dims
def _unpack_Alpha_1d(Alpha_1d):
X_1d_begin, X_1d_end = 0, 0 + num_trajectory_samples * num_x_dims
U_1d_begin, U_1d_end = X_1d_end, X_1d_end + num_trajectory_samples * num_u_dims
DT_1d_begin, DT_1d_end = U_1d_end, U_1d_end + num_trajectory_samples * num_dt_dims
X_1d = Alpha_1d[X_1d_begin:X_1d_end]
U_1d = Alpha_1d[U_1d_begin:U_1d_end]
DT_1d = Alpha_1d[DT_1d_begin:DT_1d_end]
X = X_1d.reshape((num_trajectory_samples, num_x_dims))
U = U_1d.reshape((num_trajectory_samples, num_u_dims))
DT = DT_1d.reshape((num_trajectory_samples, num_dt_dims))
return X, U, DT
def _compute_common_vals(ti, X, U, DT):
lamb_J_control_effort_ti = lamb_J_control_effort[ti]
lamb_J_x_p_waypoint_ti = lamb_J_x_p_waypoint[ti]
lamb_J_dt_ti = lamb_J_dt[ti]
lamb_g_x_waypoint_ti = lamb_g_x_waypoint[ti]
lamb_g_x_p_waypoint_ti = lamb_g_x_p_waypoint[ti]
lamb_g_dt_ti = lamb_g_dt[ti]
J_x_p_waypoint_ref_ti = matrix(J_x_p_waypoint_ref[ti]).T
J_dt_ref_ti = J_dt_ref[ti]
x_waypoint_ref_ti = matrix(X_waypoint_ref[ti]).T
x_p_waypoint_ref_ti = matrix(X_p_waypoint_ref[ti]).T
dt_ref_ti = dt_ref[ti]
x_ti = matrix(X[ti]).T
u_ti = matrix(U[ti]).T
dt_ti = DT[ti]
lamb_vals_ti = hstack([
lamb_J_control_effort_ti, lamb_J_x_p_waypoint_ti, lamb_J_dt_ti,
lamb_g_x_waypoint_ti, lamb_g_x_p_waypoint_ti, lamb_g_dt_ti
])
ref_vals_ti = hstack([
matrix(J_x_p_waypoint_ref_ti).A1, J_dt_ref_ti,
matrix(x_waypoint_ref_ti).A1,
matrix(x_p_waypoint_ref_ti).A1, dt_ref_ti
])
var_vals_ti = hstack([x_ti.A1, u_ti.A1, dt_ti])
common_vals_ti = hstack([lamb_vals_ti, ref_vals_ti, var_vals_ti])
return common_vals_ti
def _compute_sparse_jacobian_indices(ti, ti_to_ti_sparse, num_dims_gi):
ti_sparse = ti_to_ti_sparse[ti]
gi_begin = (ti_sparse + 0) * num_dims_gi
gi_end = (ti_sparse + 1) * num_dims_gi
xi_begin = (ti + 0) * num_x_dims
xi_end = (ti + 1) * num_x_dims
ui_begin = (ti + 0) * num_u_dims
ui_end = (ti + 1) * num_u_dims
dti_begin = (ti + 0) * num_dt_dims
dti_end = (ti + 1) * num_dt_dims
return gi_begin, gi_end, xi_begin, xi_end, ui_begin, ui_end, dti_begin, dti_end
# Define objective function
def _obj_func(Alpha_1d):
global snopt_major_iter_count
global snopt_obj_vals
X, U, DT = _unpack_Alpha_1d(Alpha_1d)
common_vals = c_[lamb_vals, ref_vals, X, U, DT]
const_and_xcurrent_and_xnext_and_ucurrent_and_dtcurrent_vals = c_[
const_vals[:-1], X[:-1], X[1:], U[:-1], DT[:-1]]
J_ti = J_ti_vectorized_autowrap(common_vals)
g_dynamics = quadrotor3d.g_dynamics_ti_vectorized_autowrap(
const_and_xcurrent_and_xnext_and_ucurrent_and_dtcurrent_vals)
g_x_waypoint = zeros(
(num_lamb_g_x_waypoint_nonzero, num_dims_g_x_waypoint_ti))
g_x_p_waypoint = zeros(
(num_lamb_g_x_p_waypoint_nonzero, num_dims_g_x_p_waypoint_ti))
g_dt = zeros((num_lamb_g_dt_nonzero, num_dims_g_dt_ti))
for ti in range(num_trajectory_samples):
common_vals_ti = _compute_common_vals(ti, X, U, DT)
lamb_g_x_waypoint_ti = lamb_g_x_waypoint[ti]
lamb_g_x_p_waypoint_ti = lamb_g_x_p_waypoint[ti]
lamb_g_dt_ti = lamb_g_dt[ti]
if lamb_g_x_waypoint_ti != 0:
g_x_waypoint[lamb_g_x_waypoint_ti_to_ti_sparse[
ti]] = sympyutils.evaluate_anon_func(
g_x_waypoint_ti_autowrap, common_vals_ti).T
if lamb_g_x_p_waypoint_ti != 0:
g_x_p_waypoint[lamb_g_x_p_waypoint_ti_to_ti_sparse[
ti]] = sympyutils.evaluate_anon_func(
g_x_p_waypoint_ti_autowrap, common_vals_ti).T
if lamb_g_dt_ti != 0:
g_dt[lamb_g_dt_ti_to_ti_sparse[
ti]] = sympyutils.evaluate_anon_func(
g_dt_ti_autowrap, common_vals_ti)
J = sum(J_ti)
g_1d = hstack([
matrix(g_dynamics).A1,
matrix(g_x_waypoint).A1,
matrix(g_x_p_waypoint).A1,
matrix(g_dt).A1
])
snopt_obj_vals[snopt_major_iter_count, 0] = J
snopt_obj_vals[snopt_major_iter_count,
1] = sum(norm(g_dynamics, axis=1))
snopt_major_iter_count = snopt_major_iter_count + 1
set_printoptions(suppress=True)
print "SNOPT major iteration: %d, Objective value: %f, Total g_dynamics error: %f" % (
snopt_major_iter_count, J, sum(square(g_dynamics)))
fail = 0
return J, g_1d, fail
# Define gradient function
def _grad_func(Alpha_1d, J, g_1d, compute_nonzero_only=False):
X, U, DT = _unpack_Alpha_1d(Alpha_1d)
dJ_dX = zeros((num_trajectory_samples, num_x_dims))
dJ_dU = zeros((num_trajectory_samples, num_u_dims))
dJ_dDT = zeros((num_trajectory_samples, num_dt_dims))
dgdynamics_dX = zeros(
(num_constraints_1d_g_dynamics, num_decision_vars_1d_X))
dgdynamics_dU = zeros(
(num_constraints_1d_g_dynamics, num_decision_vars_1d_U))
dgdynamics_dDT = zeros(
(num_constraints_1d_g_dynamics, num_decision_vars_1d_DT))
dgxwaypoint_dX = zeros(
(num_constraints_1d_g_x_waypoint, num_decision_vars_1d_X))
dgxwaypoint_dU = zeros(
(num_constraints_1d_g_x_waypoint, num_decision_vars_1d_U))
dgxwaypoint_dDT = zeros(
(num_constraints_1d_g_x_waypoint, num_decision_vars_1d_DT))
dgxpwaypoint_dX = zeros(
(num_constraints_1d_g_x_p_waypoint, num_decision_vars_1d_X))
dgxpwaypoint_dU = zeros(
(num_constraints_1d_g_x_p_waypoint, num_decision_vars_1d_U))
dgxpwaypoint_dDT = zeros(
(num_constraints_1d_g_x_p_waypoint, num_decision_vars_1d_DT))
dgdt_dX = zeros((num_constraints_1d_g_dt, num_decision_vars_1d_X))
dgdt_dU = zeros((num_constraints_1d_g_dt, num_decision_vars_1d_U))
dgdt_dDT = zeros((num_constraints_1d_g_dt, num_decision_vars_1d_DT))
if not compute_nonzero_only:
common_vals = c_[lamb_vals, ref_vals, X, U, DT]
const_and_xcurrent_and_xnext_and_ucurrent_and_dtcurrent_vals = c_[
const_vals[:-1], X[:-1], X[1:], U[:-1], DT[:-1]]
dJ_dX = dJti_dxti_vectorized_autowrap(common_vals)
dJ_dU = dJti_duti_vectorized_autowrap(common_vals)
dJ_dDT = dJti_ddtti_vectorized_autowrap(common_vals)
dgdynamics_dX_current_block = quadrotor3d.dgdynamicsti_dxcurrent_vectorized_autowrap(
const_and_xcurrent_and_xnext_and_ucurrent_and_dtcurrent_vals)
dgdynamics_dX_next_block = quadrotor3d.dgdynamicsti_dxnext_vectorized_autowrap(
const_and_xcurrent_and_xnext_and_ucurrent_and_dtcurrent_vals)
dgdynamics_dU_current_block = quadrotor3d.dgdynamicsti_ducurrent_vectorized_autowrap(
const_and_xcurrent_and_xnext_and_ucurrent_and_dtcurrent_vals)
dgdynamics_dDT_current_block = quadrotor3d.dgdynamicsti_ddtcurrent_vectorized_autowrap(
const_and_xcurrent_and_xnext_and_ucurrent_and_dtcurrent_vals)
for ti in range(num_trajectory_samples):
if compute_nonzero_only:
dJ_dX[ti] = 1
dJ_dU[ti] = 1
dJ_dDT[ti] = 1
for ti in range(num_constraints_g_dynamics):
gi_begin = (ti + 0) * num_dims_g_dynamics_ti
gi_end = (ti + 1) * num_dims_g_dynamics_ti
ai_x_current_begin = (ti + 0) * num_x_dims
ai_x_current_end = (ti + 1) * num_x_dims
ai_x_next_begin = (ti + 1) * num_x_dims
ai_x_next_end = (ti + 2) * num_x_dims
ai_u_current_begin = (ti + 0) * num_u_dims
ai_u_current_end = (ti + 1) * num_u_dims
ai_dt_current_begin = (ti + 0) * num_dt_dims
ai_dt_current_end = (ti + 1) * num_dt_dims
if compute_nonzero_only:
dgdynamics_dX[gi_begin:gi_end,
ai_x_current_begin:ai_x_current_end] = 1
dgdynamics_dX[gi_begin:gi_end,
ai_x_next_begin:ai_x_next_end] = 1
dgdynamics_dU[gi_begin:gi_end,
ai_u_current_begin:ai_u_current_end] = 1
dgdynamics_dDT[gi_begin:gi_end,
ai_dt_current_begin:ai_dt_current_end] = 1
else:
dgdynamics_dX[
gi_begin:gi_end, ai_x_current_begin:
ai_x_current_end] = dgdynamics_dX_current_block[ti]
dgdynamics_dX[gi_begin:gi_end, ai_x_next_begin:
ai_x_next_end] = dgdynamics_dX_next_block[ti]
dgdynamics_dU[
gi_begin:gi_end, ai_u_current_begin:
ai_u_current_end] = dgdynamics_dU_current_block[ti]
dgdynamics_dDT[gi_begin:gi_end,
ai_dt_current_begin:ai_dt_current_end] = matrix(
dgdynamics_dDT_current_block[ti]).T
for ti in range(num_trajectory_samples):
common_vals_ti = _compute_common_vals(ti, X, U, DT)
lamb_g_x_waypoint_ti = lamb_g_x_waypoint[ti]
lamb_g_x_p_waypoint_ti = lamb_g_x_p_waypoint[ti]
lamb_g_dt_ti = lamb_g_dt[ti]
if lamb_g_x_waypoint_ti != 0:
gi_begin, gi_end, xi_begin, xi_end, ui_begin, ui_end, li_begin, li_end = _compute_sparse_jacobian_indices(
ti, lamb_g_x_waypoint_ti_to_ti_sparse,
num_dims_g_x_waypoint_ti)
dgxwaypoint_dX[
gi_begin:gi_end,
xi_begin:xi_end] = sympyutils.evaluate_anon_func(
dgxwaypointti_dxti_autowrap, common_vals_ti)
if lamb_g_x_p_waypoint_ti != 0:
gi_begin, gi_end, xi_begin, xi_end, ui_begin, ui_end, li_begin, li_end = _compute_sparse_jacobian_indices(
ti, lamb_g_x_p_waypoint_ti_to_ti_sparse,
num_dims_g_x_p_waypoint_ti)
dgxpwaypoint_dX[
gi_begin:gi_end,
xi_begin:xi_end] = sympyutils.evaluate_anon_func(
dgxpwaypointti_dxti_autowrap, common_vals_ti)
if lamb_g_dt_ti != 0:
gi_begin, gi_end, xi_begin, xi_end, ui_begin, ui_end, dti_begin, dti_end = _compute_sparse_jacobian_indices(
ti, lamb_g_dt_ti_to_ti_sparse, num_dims_g_dt_ti)
dgdt_dDT[gi_begin:gi_end,
dti_begin:dti_end] = sympyutils.evaluate_anon_func(
dgdtti_ddtti_autowrap, common_vals_ti)
dJ_dAlpha_1d = hstack(
[matrix(dJ_dX).A1,
matrix(dJ_dU).A1,
matrix(dJ_dDT).A1])
dgdynamics_dAlpha = c_[dgdynamics_dX, dgdynamics_dU, dgdynamics_dDT]
dgxwaypoint_dAlpha = c_[dgxwaypoint_dX, dgxwaypoint_dU,
dgxwaypoint_dDT]
dgxpwaypoint_dAlpha = c_[dgxpwaypoint_dX, dgxpwaypoint_dU,
dgxpwaypoint_dDT]
dgdt_dAlpha = c_[dgdt_dX, dgdt_dU, dgdt_dDT]
dg_dAlpha = r_[dgdynamics_dAlpha, dgxwaypoint_dAlpha,
dgxpwaypoint_dAlpha, dgdt_dAlpha]
fail = 0
return matrix(dJ_dAlpha_1d).A, dg_dAlpha, fail
def _obj_grad_func(status, Alpha_1d, needF, needG, cu, iu, ru):
J, g_1d, fail = _obj_func(Alpha_1d)
dJ_dAlpha_1d, dg_dAlpha, fail = _grad_func(Alpha_1d, J, g_1d)
J_g_1d = hstack([J, g_1d, snopt_dummy_val])
dJ_dAlpha_dg_dAlpha = r_[dJ_dAlpha_1d, dg_dAlpha]
dJ_dAlpha_dg_dAlpha_nonzero_vals = dJ_dAlpha_dg_dAlpha[
dJ_dAlpha_dg_dAlpha_nonzero_inds]
return status, J_g_1d, dJ_dAlpha_dg_dAlpha_nonzero_vals
inf = 1.0e20
snopt = SNOPT_solver()
snopt.setOption('Verbose', False)
snopt.setOption('Solution print', False)
snopt.setOption('Major print level', 0)
snopt.setOption('Print level', 0)
snopt_obj_row = 1
snopt_num_funcs_1d = num_constraints_1d_g_dynamics + num_constraints_1d_g_x_waypoint + num_constraints_1d_g_x_p_waypoint + num_constraints_1d_g_dt + 1
snopt_num_vars_1d = num_decision_vars_1d_X + num_decision_vars_1d_U + num_decision_vars_1d_DT
snopt_dummy_val = 0.0
snopt_dummy_array = zeros((1, snopt_num_vars_1d))
global snopt_major_iter_count
global snopt_obj_vals
snopt_major_iter_count = 0
snopt_obj_vals = -1 * ones((10000, 2))
X_min = tile(x_min_ti.A1, (1, num_trajectory_samples))
X_max = tile(x_max_ti.A1, (1, num_trajectory_samples))
U_min = tile(u_min_ti.A1, (1, num_trajectory_samples))
U_max = tile(u_max_ti.A1, (1, num_trajectory_samples))
DT_min = tile(dt_min_ti, (1, num_trajectory_samples))
DT_max = tile(dt_max_ti, (1, num_trajectory_samples))
Alpha_min = hstack([matrix(X_min).A1, matrix(U_min).A1, matrix(DT_min).A1])
Alpha_max = hstack([matrix(X_max).A1, matrix(U_max).A1, matrix(DT_max).A1])
X_0 = x_feasible
U_0 = u_feasible
DT_0 = dt_feasible * ones(num_trajectory_samples)
Alpha_0 = hstack([matrix(X_0).A1, matrix(U_0).A1, matrix(DT_0).A1])
print "flashlight.trajectory_optimization.quadrotor3d_fixed_path: Calculating objective value on initial guess..."
_obj_func(Alpha_0)
J_g_1d_min = hstack([
-inf,
zeros(num_constraints_1d_g_dynamics),
zeros(num_constraints_1d_g_x_waypoint),
zeros(num_constraints_1d_g_x_p_waypoint),
zeros(num_constraints_1d_g_dt), snopt_dummy_val
])
J_g_1d_max = hstack([
inf,
zeros(num_constraints_1d_g_dynamics),
zeros(num_constraints_1d_g_x_waypoint),
zeros(num_constraints_1d_g_x_p_waypoint),
zeros(num_constraints_1d_g_dt), snopt_dummy_val
])
dJ_dAlpha_dg_dAlpha_const = r_[zeros(
(snopt_num_funcs_1d, snopt_num_vars_1d)), snopt_dummy_array]
dJ_dAlpha_dg_dAlpha_const[-1, 0] = 10e-9
dJ_dAlpha_nonzero, dg_dAlpha_nonzero, fail = _grad_func(
Alpha_0, J=None, g_1d=None, compute_nonzero_only=True)
dJ_dAlpha_dg_dAlpha_nonzero = r_[dJ_dAlpha_nonzero, dg_dAlpha_nonzero,
snopt_dummy_array]
dJ_dAlpha_dg_dAlpha_nonzero_inds = dJ_dAlpha_dg_dAlpha_nonzero.nonzero()
print "flashlight.trajectory_optimization.quadrotor3d_fixed_path: Solving optimization problem..."
sys_time_begin = time.time()
snopt.snopta(name="quadrotor_3d_fixed_path_optimization_test",
usrfun=_obj_grad_func,
x0=Alpha_0,
xlow=Alpha_min,
xupp=Alpha_max,
Flow=J_g_1d_min,
Fupp=J_g_1d_max,
ObjRow=snopt_obj_row,
A=dJ_dAlpha_dg_dAlpha_const,
G=dJ_dAlpha_dg_dAlpha_nonzero,
xnames=None,
Fnames=None)
sys_time_end = time.time()
print "flashlight.trajectory_optimization.quadrotor3d_fixed_path: Finished solving optimization problem (%.03f seconds)." % (
sys_time_end - sys_time_begin)
solver_time_end = sys_time_end
solver_time = solver_time_end - solver_time_begin
print "flashlight.trajectory_optimization.quadrotor3d_fixed_path: Total solver time was %.03f seconds." % solver_time
Alpha_opt_1d = snopt.x
X_opt, U_opt, DT_opt = _unpack_Alpha_1d(Alpha_opt_1d)
dt_opt_cumsum = cumsum(DT_opt[:-1])
t_opt = hstack(
[t_numerically_stable[0], t_numerically_stable[0] + dt_opt_cumsum])
T_final_opt = dt_opt_cumsum[-1]
return X_opt, U_opt, DT_opt, t_opt, T_final_opt, solver_time, snopt_obj_vals
sum = x[0] + x[1] + x[2]
# Nonlinear objective term only
fObj = 0.0
if mode == 0 or mode == 2:
fObj = sum**2
gObj = np.zeros(nnObj, float)
if mode == 1 or mode == 2:
gObj[0] = 2.0 * sum
gObj[1] = 2.0 * sum
gObj[2] = 2.0 * sum
return mode, fObj, gObj
snoptb = SNOPT_solver()
inf = 1.0e+20
snoptb.setOption('Infinite bound', inf)
snoptb.setOption('Print file', 'sntoyb.out')
m = 4
n = 4
nnCon = 2
nnJac = 2
nnObj = 3
# J contains the sparsity pattern of the Jacobian matrix.
# For nonlinear elements, enter any nonzero number (in this case 100).
# Linear elements must be correctly defined.
"""
import numpy as np
import scipy.sparse as sp
from optimize.snopt7 import SNOPT_solver
def dieta_fun(status, x, needF, needG, cu, iu, ru):
# LP has no nonlinear terms in the objective
F = []
G = []
return status, F, G
inf = 1.0e20
snopt = SNOPT_solver()
snopt.setOption('Print file', 'dieta.out')
snopt.setOption('Minor print level', 1)
snopt.setOption('Summary frequency', 1)
snopt.setOption('Print frequency', 1)
nF = 4
ObjRow = 4
n = 6
# We provide the linear components of the Jacobian
# matrix as a dense matrix.
A = np.array([[110, 205, 160, 160, 420, 260], [4, 32, 13, 8, 4, 14],
[2, 12, 54, 285, 22, 80], [3, 24, 13, 9, 20, 19]], float)
gObj[0] = x[6]
gObj[1] = - x[5]
gObj[2] = - x[6] + x[7]
gObj[3] = x[8]
gObj[4] = - x[7]
gObj[5] = - x[1]
gObj[6] = - x[2] + x[0]
gObj[7] = - x[4] + x[2]
gObj[8] = x[3]
return mode, fObj, gObj
inf = 1.0e+20
snoptb = SNOPT_solver()
snoptb.setOption('Infinite bound',inf)
snoptb.setOption('Specs file','snmainb.spc')
snoptb.setOption('Print file','snmainb.out')
m = 18
n = 9
nnCon = 14
ne = 52
nnJac = n
nnObj = n
bl = -inf*np.ones(n+m)
bu = inf*np.ones(n+m)
# Nonlinear constraints
return status, F
def sntoya_objFG(status,x,needF,needG,cu,iu,ru):
F = np.array([ x[1], # objective row
x[0]**2 + 4.0*x[1]**2,
(x[0] - 2.0)**2 + x[1]**2 ])
G = np.array([ 2*x[0], 8*x[1], 2*(x[0]-2), 2*x[1] ])
return status, F, G
inf = 1.0e20
snopt = SNOPT_solver()
snopt.setOption('Verbose',True)
snopt.setOption('Solution print',True)
snopt.setOption('Print file','sntoya.out')
# Either dtype works, but the names for x and F have to be of
# the correct length, else they are both ignored by SNOPT:
xNames = np.array([ ' x0', ' x1' ])
FNames = np.array([ ' F0', ' F1', ' F2' ],dtype='c')
x0 = np.array([ 1.0, 1.0 ])
xlow = np.array([ 0.0, -inf])
xupp = np.array([ inf, inf])
gObj[0] = x[6]
gObj[1] = -x[5]
gObj[2] = -x[6] + x[7]
gObj[3] = x[8]
gObj[4] = -x[7]
gObj[5] = -x[1]
gObj[6] = -x[2] + x[0]
gObj[7] = -x[4] + x[2]
gObj[8] = x[3]
return mode, fObj, gObj
inf = 1.0e+20
snoptb = SNOPT_solver()
snoptb.setOption('Infinite bound', inf)
snoptb.setOption('Specs file', 'snmainb.spc')
snoptb.setOption('Print file', 'snmainb.out')
m = 18
n = 9
nnCon = 14
ne = 52
nnJac = n
nnObj = n
bl = -inf * np.ones(n + m)
bu = inf * np.ones(n + m)
# Nonlinear constraints
def optimize_blending_function_between_two_distance_sigmas(
sigmaA,
sigmaB,
personA,
personB,
min_distA,
min_distB,
params,
constrain_at_endpoints=False):
'''
This function finds a blend between two trajectories that respect two minimum distance constraints.
'''
# Some important parameters here
nsamples = params['nsamples'] if 'nsamples' in params else 50
ndims = 6
dt = 0.01
xdot5_limit = 0.001
inf = 1.0e20
lambda_snap = 1 #(1/dt)**4 # snap must be scaled down to be comparable to position.
lambda_pos = 1
# A few derived quantities
nvars = ndims * nsamples
nconstraints_continuity = (ndims - 1) * nsamples
nconstraints_obstacles = 2 * nsamples
nconstraints = 1 + nconstraints_continuity + nconstraints_obstacles
# Solver configuration
snopt = SNOPT_solver()
snopt.setOption('Verbose', False)
snopt.setOption('Solution print', False)
snopt.setOption('Print file', 'test5.out')
snopt.setOption('Iteration limit', 8000)
snopt.setOption('Print level', 3)
snopt.setOption('Major optimality', 2e-6)
snopt.setOption('Verify level',
3) # Turn to 3 to carefully check gradiants
# 1. Set up decision variables
x = np.array([0.5] * nsamples) # Initialize to 0.5
xdot1 = np.array([0.0] * nsamples)
xdot2 = np.array([0.0] * nsamples)
xdot3 = np.array([0.0] * nsamples)
xdot4 = np.array([0.0] * nsamples)
v = np.array([0.0] * nsamples) # C4 Continuity Control Variable
x0 = np.matrix(np.c_[x, xdot1, xdot2, xdot3, xdot4,
v]).A1 # Interleave [x[0],xdot1[0],xdot2[0]...]
# 2. Set up the bounds on x
low_x = np.array([0.0] * nsamples) # X must be greater or equal to 0
low_xdot1 = np.array([-inf] * nsamples)
low_xdot2 = np.array([-inf] * nsamples)
low_xdot3 = np.array([-inf] * nsamples)
low_xdot4 = np.array([-inf] * nsamples)
low_v = np.array([-xdot5_limit] *
nsamples) # Bound control variable arbitrarily
if constrain_at_endpoints:
low_x[0] = 0.5
low_x[nsamples - 1] = 0.5
xlow = np.matrix(np.c_[low_x, low_xdot1, low_xdot2, low_xdot3, low_xdot4,
low_v]).A1 # Interleave [x[0],xdot1[0],xdot2[0]...]
upp_x = np.array([1.0] * nsamples) # X must be greater or equal to 0
upp_xdot1 = np.array([inf] * nsamples)
upp_xdot2 = np.array([inf] * nsamples)
upp_xdot3 = np.array([inf] * nsamples)
upp_xdot4 = np.array([inf] * nsamples)
upp_v = np.array([xdot5_limit] *
nsamples) # Bound control variable arbitrarily
if constrain_at_endpoints:
upp_x[0] = 0.5
upp_x[nsamples - 1] = 0.5
xupp = np.matrix(np.c_[upp_x, upp_xdot1, upp_xdot2, upp_xdot3, upp_xdot4,
upp_v]).A1 # Interleave [x[0],xdot1[0],xdot2[0]...]
# 3. Set up the objective function
M = np.array([[0, 1, 0, 0, 0], [0, 0, 1, 0, 0], [0, 0, 0, 1, 0],
[0, 0, 0, 0, 1], [0, 0, 0, 0, 0]])
N = np.array([0, 0, 0, 0, 1])
def grad_function(xM, compute_nonzero_only=False, compute_linear=False):
G = np.zeros((nconstraints, nvars))
# Set up the jacobian structure of the cost function.
# This only impacts the w_i and wdot4_i variables
obj_col = G[0, :]
if not compute_nonzero_only:
obj_col[::6] = 2 * dt * lambda_pos * (xM[:, 0] - 0.5)
obj_col[4::6] = 2 * dt * lambda_snap * xM[:, 4]
elif not compute_linear:
obj_col[::6] = 1
obj_col[4::6] = 1
if compute_linear:
# The C4 continuity constraint is linear
stupidcounter = 0
add_to_fi = 0
for fi in range(1, nconstraints_continuity -
5): # Looping over the objective function
fi_row = G[fi, :]
fi += add_to_fi
fi_row[fi - 1] = 1
fi_row[fi] = dt
fi_row[fi + 5] = -1
stupidcounter += 1
if stupidcounter == 5:
add_to_fi += 1
stupidcounter = 0
return G
def calc_obj(xM):
# our objective is the sum of
# the L2 norm of our position error away from 0.5
# the L2 norm of our 4th derivative error away from 0
obj_pos = dt * np.sum((xM[:, 0] - 0.5)**2)
obj_snap = dt * np.sum((xM[:, 4])**2)
objective = lambda_pos * obj_pos + lambda_snap * obj_snap
return (objective, obj_pos, obj_snap)
def calc_obstacle_constraints(xM):
blend = xM[:, 0]
sigmaBlended = (blend[:, np.newaxis] * sigmaA +
(1 - blend)[:, np.newaxis] * sigmaB)
constraintA = la.norm(sigmaBlended - personA, axis=1) - min_distA
constraintB = la.norm(sigmaBlended - personB, axis=1) - min_distB
return np.r_[constraintA, constraintB]
def blend_test3_objFG(status, x, needF, needG, cu, iu, ru):
xM = x.reshape(nsamples, ndims)
objective, obj_pos, obj_snap = calc_obj(xM)
# Evaluate the current continuity constraints
continuity_x = np.zeros((nsamples, 5))
for i in range(nsamples - 1):
si = xM[i, 0:5]
vi = xM[i, 5]
si1 = xM[i + 1, 0:5]
continuity_x[i] = si + (M.dot(si) + N.dot(vi)) * dt - si1
continuity_x = np.matrix(continuity_x).A1
obstacles = calc_obstacle_constraints(xM)
F = np.concatenate(([objective], continuity_x, obstacles))
#G = grad_function(xM)
return status, F #, G[G_nonzero_inds]
# 4. Set up bounds on F
# [ objectivec can be anything, equal-to-zero for continuity, greater-than-0 for obstacles along traj]
low_F = np.concatenate(
([-inf], np.array([0, 0, 0, 0, 0] * nsamples), [0, 0] * nsamples))
upp_F = np.concatenate(
([inf], np.array([0, 0, 0, 0, 0] * nsamples), [inf, inf] * nsamples))
# Matrix uses fortran numbering or something
ObjRow = 1
# Set up the linear and nonlinear structure of the jacobian matrix
xM = x0.reshape(nsamples, ndims)
G = grad_function(xM, compute_nonzero_only=True, compute_linear=False)
G_nonzero_inds = G.nonzero()
A = grad_function(xM, compute_nonzero_only=True, compute_linear=True)
# Now we solve
a = time.time()
snopt.snopta(name='blend_test3',
usrfun=blend_test3_objFG,
x0=x0,
xlow=xlow,
xupp=xupp,
Flow=low_F,
Fupp=upp_F,
ObjRow=ObjRow)
b = time.time()
print "Solved in %.4fs" % (b - a)
print "Value of objective function: %.8f" % snopt.F[0]
print " lambda_pos: %f, lambda_snap: %f, " % (lambda_pos, lambda_snap)
print " objective: %f, obj_pos: %f, obj_snap: %f" % calc_obj(xM)
xM = snopt.x.reshape(nsamples, ndims)
return (xM, snopt)
def optimize(p_eval,psi_eval, \
t_nominal,user_progress_nominal,dt_nominal, \
const_vals_ti, \
x_min_ti,x_max_ti, \
u_min_ti,u_max_ti):
assert allclose(psi_eval,0.0)
print "flashlight.trajectory_optimization.quadrotor3d_direct_transcription_nonconst_dt: Initializing optimization problem..."
sys_time_begin = time.time()
solver_time_begin = sys_time_begin
#
# find numerically stable and feasible trajectories to initialize the solver
#
numerically_stable_infeasible_trajectory = quadrotor3d_gaussian_time_stretch.optimize_numerically_stable_infeasible( p_eval,psi_eval, \
t_nominal,user_progress_nominal,dt_nominal, \
x_min_ti,x_max_ti, \
u_min_ti,u_max_ti, \
max_stretch_iters_numerically_stable, \
gauss_width_in_terms_of_dt_numerically_stable, \
gauss_max_in_terms_of_dt_numerically_stable, \
0 )
x_numerically_stable,u_numerically_stable,t_numerically_stable,user_progress_numerically_stable,dt_numerically_stable = numerically_stable_infeasible_trajectory
if use_gaussian_time_stretching_for_feasible:
# use gaussian time stretching to find a feasible trajectory
feasible_trajectory = quadrotor3d_gaussian_time_stretch.optimize_feasible( p_eval,psi_eval, \
t_numerically_stable,user_progress_numerically_stable,dt_numerically_stable, \
x_min_ti,x_max_ti, \
u_min_ti,u_max_ti, \
max_stretch_iters_feasible, \
gauss_width_in_terms_of_dt_feasible, \
gauss_max_in_terms_of_dt_feasible, \
extra_iters_feasible )
x_feasible,u_feasible,t_feasible,user_progress_feasible,dt_feasible = feasible_trajectory
else:
# use uniform time stretching to find a feasible trajectory
p_nominal, _, _, _ = curveutils.reparameterize_curve( p_eval, user_progress_nominal )
psi_nominal, _, _, _ = curveutils.reparameterize_curve( psi_eval, user_progress_nominal )
feasible_trajectory = quadrotor3d_uniform_time_stretch.optimize_feasible( p_nominal,psi_nominal,dt_nominal, \
x_min_ti,x_max_ti, \
u_min_ti,u_max_ti, \
max_bin_search_iters_feasible, \
dt_upper_init_feasible )
x_feasible,u_feasible,dt_scale_feasible = feasible_trajectory
t_feasible = t_nominal*dt_scale_feasible*dt_scale_extra_stretch_feasible
user_progress_feasible = user_progress_nominal
dt_feasible = dt_nominal*dt_scale_feasible*dt_scale_extra_stretch_feasible
# return user_progress_numerically_stable,None,None,None,None,t_numerically_stable,t_numerically_stable[-1]
# return user_progress_feasible,None,None,None,None,t_feasible,t_feasible[-1]
sys_time_end = time.time()
print "flashlight.optimize.quadrotor3d_fixed_path: Finished initializing optimization problem (%.03f seconds)." % (sys_time_end - sys_time_begin)
#
# set up optimization problem constants
#
num_trajectory_samples = p_eval.shape[0]
num_x_dims = quadrotor3d.num_x_dims
num_u_dims = quadrotor3d.num_u_dims
num_dt_dims = 1
num_alpha_dims = num_x_dims + num_u_dims + num_dt_dims
x_p_inds = arange(0,3)
x_e_inds = arange(3,6)
num_x_p_inds = x_p_inds.size
# soft control effort constraints
lamb_J_control_effort = 0.0*ones(num_trajectory_samples)
# soft position waypoint constraints
num_dims_J_x_p_waypoint_ref_ti = 3
lamb_J_x_p_waypoint = 0.01*ones(num_trajectory_samples)
J_x_p_waypoint_ref = x_numerically_stable[:,0:3]
# soft dt constraints
num_dims_J_dt_ref_ti = 1
lamb_J_dt = 0.0001*ones(num_trajectory_samples)
J_dt_ref = dt_numerically_stable*ones(num_trajectory_samples)
# hard dynamics constraints
num_dims_g_dynamics_ti = num_x_dims
# hard state space waypoint constraints
num_dims_g_x_waypoint_ti = num_x_dims
num_dims_x_waypoint_ref_ti = num_x_dims
lamb_g_x_waypoint = zeros(num_trajectory_samples)
lamb_g_x_waypoint[[0,-1]] = 1
X_waypoint_ref = zeros((num_trajectory_samples,num_dims_x_waypoint_ref_ti))
X_waypoint_ref[0] = array([ p_eval[0,0], p_eval[0,1], p_eval[0,2], 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ])
X_waypoint_ref[-1] = array([ p_eval[-1,0], p_eval[-1,1], p_eval[-1,2], 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ])
lamb_g_x_waypoint_nonzero = nonzero(lamb_g_x_waypoint)[0]
num_lamb_g_x_waypoint_nonzero = len(lamb_g_x_waypoint_nonzero)
lamb_g_x_waypoint_ti_to_ti_sparse = -1*ones_like(lamb_g_x_waypoint,dtype=int32)
lamb_g_x_waypoint_ti_to_ti_sparse[lamb_g_x_waypoint_nonzero] = arange(num_lamb_g_x_waypoint_nonzero)
# hard position waypoint constraints
num_dims_g_x_p_waypoint_ti = num_x_p_inds
num_dims_x_p_waypoint_ref_ti = num_x_p_inds
lamb_g_x_p_waypoint = zeros(num_trajectory_samples)
X_p_waypoint_ref = zeros((num_trajectory_samples,num_dims_x_p_waypoint_ref_ti))
lamb_g_x_p_waypoint_nonzero = nonzero(lamb_g_x_p_waypoint)[0]
num_lamb_g_x_p_waypoint_nonzero = len(lamb_g_x_p_waypoint_nonzero)
lamb_g_x_p_waypoint_ti_to_ti_sparse = -1*ones_like(lamb_g_x_p_waypoint,dtype=int32)
lamb_g_x_p_waypoint_ti_to_ti_sparse[lamb_g_x_p_waypoint_nonzero] = arange(num_lamb_g_x_p_waypoint_nonzero)
# hard dt constraints
num_dims_g_dt_ti = 1
num_dims_dt_ref_ti = 1
lamb_g_dt = zeros(num_trajectory_samples)
dt_ref = zeros(num_trajectory_samples)
lamb_g_dt_nonzero = nonzero(lamb_g_dt)[0]
num_lamb_g_dt_nonzero = len(lamb_g_dt_nonzero)
lamb_g_dt_ti_to_ti_sparse = -1*ones_like(lamb_g_dt,dtype=int32)
lamb_g_dt_ti_to_ti_sparse[lamb_g_dt_nonzero] = arange(num_lamb_g_dt_nonzero)
dt_min_ti = dt_numerically_stable*0.45
dt_max_ti = dt_feasible*1.55
# stack all the const, lamb, and ref values
const_vals = tile(const_vals_ti,(num_trajectory_samples,1))
lamb_vals = c_[ lamb_J_control_effort, lamb_J_x_p_waypoint, lamb_J_dt, lamb_g_x_waypoint, lamb_g_x_p_waypoint, lamb_g_dt ]
ref_vals = c_[ J_x_p_waypoint_ref, J_dt_ref, X_waypoint_ref, X_p_waypoint_ref, dt_ref ]
# number of constraints and decision variables
num_constraints_g_dynamics = num_trajectory_samples-1
num_dims_g_dynamics_ti = num_x_dims
num_constraints_1d_g_dynamics = num_constraints_g_dynamics*num_dims_g_dynamics_ti
num_constraints_1d_g_x_waypoint = num_lamb_g_x_waypoint_nonzero*num_dims_g_x_waypoint_ti
num_constraints_1d_g_x_p_waypoint = num_lamb_g_x_p_waypoint_nonzero*num_dims_g_x_p_waypoint_ti
num_constraints_1d_g_dt = num_lamb_g_dt_nonzero*num_dims_g_dt_ti
num_decision_vars_1d_X = num_trajectory_samples*num_x_dims
num_decision_vars_1d_U = num_trajectory_samples*num_u_dims
num_decision_vars_1d_DT = num_trajectory_samples*num_dt_dims
def _unpack_Alpha_1d(Alpha_1d):
X_1d_begin,X_1d_end = 0, 0 + num_trajectory_samples*num_x_dims
U_1d_begin,U_1d_end = X_1d_end, X_1d_end + num_trajectory_samples*num_u_dims
DT_1d_begin,DT_1d_end = U_1d_end, U_1d_end + num_trajectory_samples*num_dt_dims
X_1d = Alpha_1d[X_1d_begin:X_1d_end]
U_1d = Alpha_1d[U_1d_begin:U_1d_end]
DT_1d = Alpha_1d[DT_1d_begin:DT_1d_end]
X = X_1d.reshape((num_trajectory_samples,num_x_dims))
U = U_1d.reshape((num_trajectory_samples,num_u_dims))
DT = DT_1d.reshape((num_trajectory_samples,num_dt_dims))
return X,U,DT
def _compute_common_vals(ti,X,U,DT):
lamb_J_control_effort_ti = lamb_J_control_effort[ti]
lamb_J_x_p_waypoint_ti = lamb_J_x_p_waypoint[ti]
lamb_J_dt_ti = lamb_J_dt[ti]
lamb_g_x_waypoint_ti = lamb_g_x_waypoint[ti]
lamb_g_x_p_waypoint_ti = lamb_g_x_p_waypoint[ti]
lamb_g_dt_ti = lamb_g_dt[ti]
J_x_p_waypoint_ref_ti = matrix(J_x_p_waypoint_ref[ti]).T
J_dt_ref_ti = J_dt_ref[ti]
x_waypoint_ref_ti = matrix(X_waypoint_ref[ti]).T
x_p_waypoint_ref_ti = matrix(X_p_waypoint_ref[ti]).T
dt_ref_ti = dt_ref[ti]
x_ti = matrix(X[ti]).T
u_ti = matrix(U[ti]).T
dt_ti = DT[ti]
lamb_vals_ti = hstack( [ lamb_J_control_effort_ti, lamb_J_x_p_waypoint_ti, lamb_J_dt_ti, lamb_g_x_waypoint_ti, lamb_g_x_p_waypoint_ti, lamb_g_dt_ti ] )
ref_vals_ti = hstack( [ matrix(J_x_p_waypoint_ref_ti).A1, J_dt_ref_ti, matrix(x_waypoint_ref_ti).A1, matrix(x_p_waypoint_ref_ti).A1, dt_ref_ti ] )
var_vals_ti = hstack( [ x_ti.A1, u_ti.A1, dt_ti ] )
common_vals_ti = hstack( [ lamb_vals_ti, ref_vals_ti, var_vals_ti ] )
return common_vals_ti
def _compute_sparse_jacobian_indices(ti,ti_to_ti_sparse,num_dims_gi):
ti_sparse = ti_to_ti_sparse[ti]
gi_begin = (ti_sparse+0)*num_dims_gi
gi_end = (ti_sparse+1)*num_dims_gi
xi_begin = (ti+0)*num_x_dims
xi_end = (ti+1)*num_x_dims
ui_begin = (ti+0)*num_u_dims
ui_end = (ti+1)*num_u_dims
dti_begin = (ti+0)*num_dt_dims
dti_end = (ti+1)*num_dt_dims
return gi_begin,gi_end,xi_begin,xi_end,ui_begin,ui_end,dti_begin,dti_end
# Define objective function
def _obj_func(Alpha_1d):
global snopt_major_iter_count
global snopt_obj_vals
X,U,DT = _unpack_Alpha_1d(Alpha_1d)
common_vals = c_[ lamb_vals, ref_vals, X, U, DT ]
const_and_xcurrent_and_xnext_and_ucurrent_and_dtcurrent_vals = c_[ const_vals[:-1], X[:-1], X[1:], U[:-1], DT[:-1] ]
J_ti = J_ti_vectorized_autowrap(common_vals)
g_dynamics = quadrotor3d.g_dynamics_ti_vectorized_autowrap(const_and_xcurrent_and_xnext_and_ucurrent_and_dtcurrent_vals)
g_x_waypoint = zeros((num_lamb_g_x_waypoint_nonzero, num_dims_g_x_waypoint_ti))
g_x_p_waypoint = zeros((num_lamb_g_x_p_waypoint_nonzero, num_dims_g_x_p_waypoint_ti))
g_dt = zeros((num_lamb_g_dt_nonzero, num_dims_g_dt_ti))
for ti in range(num_trajectory_samples):
common_vals_ti = _compute_common_vals(ti,X,U,DT)
lamb_g_x_waypoint_ti = lamb_g_x_waypoint[ti]
lamb_g_x_p_waypoint_ti = lamb_g_x_p_waypoint[ti]
lamb_g_dt_ti = lamb_g_dt[ti]
if lamb_g_x_waypoint_ti != 0: g_x_waypoint[lamb_g_x_waypoint_ti_to_ti_sparse[ti]] = sympyutils.evaluate_anon_func( g_x_waypoint_ti_autowrap, common_vals_ti ).T
if lamb_g_x_p_waypoint_ti != 0: g_x_p_waypoint[lamb_g_x_p_waypoint_ti_to_ti_sparse[ti]] = sympyutils.evaluate_anon_func( g_x_p_waypoint_ti_autowrap, common_vals_ti ).T
if lamb_g_dt_ti != 0: g_dt[lamb_g_dt_ti_to_ti_sparse[ti]] = sympyutils.evaluate_anon_func( g_dt_ti_autowrap, common_vals_ti )
J = sum(J_ti)
g_1d = hstack( [ matrix(g_dynamics).A1, matrix(g_x_waypoint).A1, matrix(g_x_p_waypoint).A1, matrix(g_dt).A1 ] )
snopt_obj_vals[snopt_major_iter_count,0] = J
snopt_obj_vals[snopt_major_iter_count,1] = sum(norm(g_dynamics,axis=1))
snopt_major_iter_count = snopt_major_iter_count+1
set_printoptions(suppress=True)
print "SNOPT major iteration: %d, Objective value: %f, Total g_dynamics error: %f" % (snopt_major_iter_count,J,sum(square(g_dynamics)))
fail = 0
return J, g_1d, fail
# Define gradient function
def _grad_func(Alpha_1d, J, g_1d, compute_nonzero_only=False):
X,U,DT = _unpack_Alpha_1d(Alpha_1d)
dJ_dX = zeros((num_trajectory_samples,num_x_dims))
dJ_dU = zeros((num_trajectory_samples,num_u_dims))
dJ_dDT = zeros((num_trajectory_samples,num_dt_dims))
dgdynamics_dX = zeros((num_constraints_1d_g_dynamics,num_decision_vars_1d_X))
dgdynamics_dU = zeros((num_constraints_1d_g_dynamics,num_decision_vars_1d_U))
dgdynamics_dDT = zeros((num_constraints_1d_g_dynamics,num_decision_vars_1d_DT))
dgxwaypoint_dX = zeros((num_constraints_1d_g_x_waypoint,num_decision_vars_1d_X))
dgxwaypoint_dU = zeros((num_constraints_1d_g_x_waypoint,num_decision_vars_1d_U))
dgxwaypoint_dDT = zeros((num_constraints_1d_g_x_waypoint,num_decision_vars_1d_DT))
dgxpwaypoint_dX = zeros((num_constraints_1d_g_x_p_waypoint,num_decision_vars_1d_X))
dgxpwaypoint_dU = zeros((num_constraints_1d_g_x_p_waypoint,num_decision_vars_1d_U))
dgxpwaypoint_dDT = zeros((num_constraints_1d_g_x_p_waypoint,num_decision_vars_1d_DT))
dgdt_dX = zeros((num_constraints_1d_g_dt,num_decision_vars_1d_X))
dgdt_dU = zeros((num_constraints_1d_g_dt,num_decision_vars_1d_U))
dgdt_dDT = zeros((num_constraints_1d_g_dt,num_decision_vars_1d_DT))
if not compute_nonzero_only:
common_vals = c_[ lamb_vals, ref_vals, X, U, DT ]
const_and_xcurrent_and_xnext_and_ucurrent_and_dtcurrent_vals = c_[ const_vals[:-1], X[:-1], X[1:], U[:-1], DT[:-1] ]
dJ_dX = dJti_dxti_vectorized_autowrap(common_vals)
dJ_dU = dJti_duti_vectorized_autowrap(common_vals)
dJ_dDT = dJti_ddtti_vectorized_autowrap(common_vals)
dgdynamics_dX_current_block = quadrotor3d.dgdynamicsti_dxcurrent_vectorized_autowrap(const_and_xcurrent_and_xnext_and_ucurrent_and_dtcurrent_vals)
dgdynamics_dX_next_block = quadrotor3d.dgdynamicsti_dxnext_vectorized_autowrap(const_and_xcurrent_and_xnext_and_ucurrent_and_dtcurrent_vals)
dgdynamics_dU_current_block = quadrotor3d.dgdynamicsti_ducurrent_vectorized_autowrap(const_and_xcurrent_and_xnext_and_ucurrent_and_dtcurrent_vals)
dgdynamics_dDT_current_block = quadrotor3d.dgdynamicsti_ddtcurrent_vectorized_autowrap(const_and_xcurrent_and_xnext_and_ucurrent_and_dtcurrent_vals)
for ti in range(num_trajectory_samples):
if compute_nonzero_only:
dJ_dX[ti] = 1
dJ_dU[ti] = 1
dJ_dDT[ti] = 1
for ti in range(num_constraints_g_dynamics):
gi_begin = (ti+0)*num_dims_g_dynamics_ti
gi_end = (ti+1)*num_dims_g_dynamics_ti
ai_x_current_begin = (ti+0)*num_x_dims
ai_x_current_end = (ti+1)*num_x_dims
ai_x_next_begin = (ti+1)*num_x_dims
ai_x_next_end = (ti+2)*num_x_dims
ai_u_current_begin = (ti+0)*num_u_dims
ai_u_current_end = (ti+1)*num_u_dims
ai_dt_current_begin = (ti+0)*num_dt_dims
ai_dt_current_end = (ti+1)*num_dt_dims
if compute_nonzero_only:
dgdynamics_dX[gi_begin:gi_end,ai_x_current_begin:ai_x_current_end] = 1
dgdynamics_dX[gi_begin:gi_end,ai_x_next_begin:ai_x_next_end] = 1
dgdynamics_dU[gi_begin:gi_end,ai_u_current_begin:ai_u_current_end] = 1
dgdynamics_dDT[gi_begin:gi_end,ai_dt_current_begin:ai_dt_current_end] = 1
else:
dgdynamics_dX[gi_begin:gi_end,ai_x_current_begin:ai_x_current_end] = dgdynamics_dX_current_block[ti]
dgdynamics_dX[gi_begin:gi_end,ai_x_next_begin:ai_x_next_end] = dgdynamics_dX_next_block[ti]
dgdynamics_dU[gi_begin:gi_end,ai_u_current_begin:ai_u_current_end] = dgdynamics_dU_current_block[ti]
dgdynamics_dDT[gi_begin:gi_end,ai_dt_current_begin:ai_dt_current_end] = matrix(dgdynamics_dDT_current_block[ti]).T
for ti in range(num_trajectory_samples):
common_vals_ti = _compute_common_vals(ti,X,U,DT)
lamb_g_x_waypoint_ti = lamb_g_x_waypoint[ti]
lamb_g_x_p_waypoint_ti = lamb_g_x_p_waypoint[ti]
lamb_g_dt_ti = lamb_g_dt[ti]
if lamb_g_x_waypoint_ti != 0:
gi_begin,gi_end,xi_begin,xi_end,ui_begin,ui_end,li_begin,li_end = _compute_sparse_jacobian_indices(ti,lamb_g_x_waypoint_ti_to_ti_sparse,num_dims_g_x_waypoint_ti)
dgxwaypoint_dX[gi_begin:gi_end,xi_begin:xi_end] = sympyutils.evaluate_anon_func( dgxwaypointti_dxti_autowrap, common_vals_ti )
if lamb_g_x_p_waypoint_ti != 0:
gi_begin,gi_end,xi_begin,xi_end,ui_begin,ui_end,li_begin,li_end = _compute_sparse_jacobian_indices(ti,lamb_g_x_p_waypoint_ti_to_ti_sparse,num_dims_g_x_p_waypoint_ti)
dgxpwaypoint_dX[gi_begin:gi_end,xi_begin:xi_end] = sympyutils.evaluate_anon_func( dgxpwaypointti_dxti_autowrap, common_vals_ti )
if lamb_g_dt_ti != 0:
gi_begin,gi_end,xi_begin,xi_end,ui_begin,ui_end,dti_begin,dti_end = _compute_sparse_jacobian_indices(ti,lamb_g_dt_ti_to_ti_sparse,num_dims_g_dt_ti)
dgdt_dDT[gi_begin:gi_end,dti_begin:dti_end] = sympyutils.evaluate_anon_func( dgdtti_ddtti_autowrap, common_vals_ti )
dJ_dAlpha_1d = hstack( [ matrix(dJ_dX).A1, matrix(dJ_dU).A1, matrix(dJ_dDT).A1 ] )
dgdynamics_dAlpha = c_[ dgdynamics_dX, dgdynamics_dU, dgdynamics_dDT ]
dgxwaypoint_dAlpha = c_[ dgxwaypoint_dX, dgxwaypoint_dU, dgxwaypoint_dDT ]
dgxpwaypoint_dAlpha = c_[ dgxpwaypoint_dX, dgxpwaypoint_dU, dgxpwaypoint_dDT ]
dgdt_dAlpha = c_[ dgdt_dX, dgdt_dU, dgdt_dDT ]
dg_dAlpha = r_[ dgdynamics_dAlpha, dgxwaypoint_dAlpha, dgxpwaypoint_dAlpha, dgdt_dAlpha ]
fail = 0
return matrix(dJ_dAlpha_1d).A, dg_dAlpha, fail
def _obj_grad_func(status,Alpha_1d,needF,needG,cu,iu,ru):
J, g_1d, fail = _obj_func(Alpha_1d)
dJ_dAlpha_1d, dg_dAlpha, fail = _grad_func(Alpha_1d,J,g_1d)
J_g_1d = hstack( [ J, g_1d, snopt_dummy_val ] )
dJ_dAlpha_dg_dAlpha = r_[ dJ_dAlpha_1d, dg_dAlpha ]
dJ_dAlpha_dg_dAlpha_nonzero_vals = dJ_dAlpha_dg_dAlpha[dJ_dAlpha_dg_dAlpha_nonzero_inds]
return status, J_g_1d, dJ_dAlpha_dg_dAlpha_nonzero_vals
inf = 1.0e20
snopt = SNOPT_solver()
snopt.setOption('Verbose',False)
snopt.setOption('Solution print',False)
snopt.setOption('Major print level',0)
snopt.setOption('Print level',0)
snopt_obj_row = 1
snopt_num_funcs_1d = num_constraints_1d_g_dynamics + num_constraints_1d_g_x_waypoint + num_constraints_1d_g_x_p_waypoint + num_constraints_1d_g_dt + 1
snopt_num_vars_1d = num_decision_vars_1d_X + num_decision_vars_1d_U + num_decision_vars_1d_DT
snopt_dummy_val = 0.0
snopt_dummy_array = zeros((1,snopt_num_vars_1d))
global snopt_major_iter_count
global snopt_obj_vals
snopt_major_iter_count = 0
snopt_obj_vals = -1*ones((10000,2))
X_min = tile(x_min_ti.A1,(1,num_trajectory_samples))
X_max = tile(x_max_ti.A1,(1,num_trajectory_samples))
U_min = tile(u_min_ti.A1,(1,num_trajectory_samples))
U_max = tile(u_max_ti.A1,(1,num_trajectory_samples))
DT_min = tile(dt_min_ti,(1,num_trajectory_samples))
DT_max = tile(dt_max_ti,(1,num_trajectory_samples))
Alpha_min = hstack( [ matrix(X_min).A1, matrix(U_min).A1, matrix(DT_min).A1 ] )
Alpha_max = hstack( [ matrix(X_max).A1, matrix(U_max).A1, matrix(DT_max).A1 ] )
X_0 = x_feasible
U_0 = u_feasible
DT_0 = dt_feasible*ones(num_trajectory_samples)
Alpha_0 = hstack( [ matrix(X_0).A1, matrix(U_0).A1, matrix(DT_0).A1 ] )
print "flashlight.trajectory_optimization.quadrotor3d_fixed_path: Calculating objective value on initial guess..."
_obj_func(Alpha_0)
J_g_1d_min = hstack( [ -inf, zeros(num_constraints_1d_g_dynamics), zeros(num_constraints_1d_g_x_waypoint), zeros(num_constraints_1d_g_x_p_waypoint), zeros(num_constraints_1d_g_dt), snopt_dummy_val ] )
J_g_1d_max = hstack( [ inf, zeros(num_constraints_1d_g_dynamics), zeros(num_constraints_1d_g_x_waypoint), zeros(num_constraints_1d_g_x_p_waypoint), zeros(num_constraints_1d_g_dt), snopt_dummy_val ] )
dJ_dAlpha_dg_dAlpha_const = r_[ zeros((snopt_num_funcs_1d,snopt_num_vars_1d)), snopt_dummy_array ]
dJ_dAlpha_dg_dAlpha_const[-1,0] = 10e-9
dJ_dAlpha_nonzero, dg_dAlpha_nonzero, fail = _grad_func(Alpha_0, J=None, g_1d=None, compute_nonzero_only=True)
dJ_dAlpha_dg_dAlpha_nonzero = r_[ dJ_dAlpha_nonzero, dg_dAlpha_nonzero, snopt_dummy_array ]
dJ_dAlpha_dg_dAlpha_nonzero_inds = dJ_dAlpha_dg_dAlpha_nonzero.nonzero()
print "flashlight.trajectory_optimization.quadrotor3d_fixed_path: Solving optimization problem..."
sys_time_begin = time.time()
snopt.snopta(
name="quadrotor_3d_fixed_path_optimization_test",
usrfun=_obj_grad_func,
x0=Alpha_0,
xlow=Alpha_min,
xupp=Alpha_max,
Flow=J_g_1d_min,
Fupp=J_g_1d_max,
ObjRow=snopt_obj_row,
A=dJ_dAlpha_dg_dAlpha_const,
G=dJ_dAlpha_dg_dAlpha_nonzero,
xnames=None,
Fnames=None)
sys_time_end = time.time()
print "flashlight.trajectory_optimization.quadrotor3d_fixed_path: Finished solving optimization problem (%.03f seconds)." % (sys_time_end - sys_time_begin)
solver_time_end = sys_time_end
solver_time = solver_time_end - solver_time_begin
print "flashlight.trajectory_optimization.quadrotor3d_fixed_path: Total solver time was %.03f seconds." % solver_time
Alpha_opt_1d = snopt.x
X_opt,U_opt,DT_opt = _unpack_Alpha_1d(Alpha_opt_1d)
dt_opt_cumsum = cumsum(DT_opt[:-1])
t_opt = hstack( [ t_numerically_stable[0], t_numerically_stable[0] + dt_opt_cumsum ] )
T_final_opt = dt_opt_cumsum[-1]
return X_opt,U_opt,DT_opt,t_opt,T_final_opt,solver_time,snopt_obj_vals
def __init__(self, conf, load_path=None):
tf_random_seed = None
nonlinearity = tf.nn.relu
self.keep_prob_train_val = 1.0
self.floatX = 'float32'
def unif_fanin_mat(shape, name):
b = np.sqrt(3 * self.keep_prob_train_val / shape[0])
initial = tf.random_uniform(shape, minval=-b, maxval=b, seed=tf_random_seed, dtype=self.floatX)
return tf.Variable(initial, name=name)
def bias(shape, name):
initial = tf.constant(0.1, shape=shape, dtype=self.floatX)
return tf.Variable(initial, name=name)
self.snopt = SNOPT_solver()
self.snopt.setOption('Major print level', 0)
#self.snopt.setOption('Major feasibility', 1.0e-6)
#self.snopt.setOption('Minor feasibility', 1.0e-6)
#self.snopt.setOption('Major optimality', 1.0e-6)
#self.snopt.setOption('Linesearch tolerance', 0.9)
#self.snopt.setOption('Major iterations', 100)
#self.snopt.setOption('Minor iterations', 50)
#self.snopt.setOption('Scale option', 0)
#self.snopt_max_count = conf['snopt_max_count']
self.snopt.setOption('Iteration limit', conf['snopt_max_count'])
self.profiler = Profiler()
self.n_s = conf['n_s']
self.n_a = conf['n_a']
self.n_sa = self.n_s + self.n_a
n_hidden = conf['n_hidden']
self.n_1 = n_hidden
self.n_2 = n_hidden
self.n_q = conf['n_q']
error_functions = [tf.square, tf.abs]
self.error_function = error_functions[conf['error_type']]
self.one_layer_only = conf['one_layer_only']
self.n_minibatch = conf['minibatch_size']
self.n_batches = conf['num_batches']
self.n_megabatch = self.n_minibatch * self.n_batches
self.max_a_min_iters = 5
self.max_abs_torque = conf['max_torque']
#self.a_tolerance = conf['a_tolerance']
self.max_torques = np.array([[self.max_abs_torque]], dtype=self.floatX)
self.max_torques_p = np.ones((self.n_megabatch,1)) * np.array([[self.max_abs_torque]], dtype=self.floatX)
self.min_torques = np.array([[-self.max_abs_torque]], dtype=self.floatX)
self.min_torques_p = np.ones((self.n_megabatch,1)) * np.array([[-self.max_abs_torque]], dtype=self.floatX)
self.set_standardizer((np.zeros(self.n_sa), np.ones(self.n_sa)),
(np.zeros(self.n_q), np.ones(self.n_q)))
self.sess = tf.Session()
self.keep_prob = tf.placeholder(self.floatX)
self.sa_learn = tf.placeholder(self.floatX, shape=[None,self.n_sa])
self.W_sa_1 = unif_fanin_mat([self.n_sa, self.n_1], 'W_sa_1')
self.b_1 = bias([self.n_1], 'b_1')
self.W_1_2 = unif_fanin_mat([self.n_1, self.n_2], 'W_1_2')
self.b_2 = bias([self.n_2], 'b_2')
self.W_2_q = unif_fanin_mat([self.n_2,self.n_q], 'W_2_q')
self.b_q = bias([self.n_q], 'b_q')
name_var_pairs = zip(['W_sa_1', 'b_1', 'b_q'],
[self.W_sa_1, self.b_1, self.b_q])
#if not self.one_layer_only:
name_var_pairs.extend(zip(['W_1_2', 'b_2', 'W_2_q'], [self.W_1_2, self.b_2, self.W_2_q]))
self.name_var_dict = {i:j for (i,j) in name_var_pairs}
# # run collapse once to set number of params
# self.collapse_params()
def q_from_input(i):
o1 = tf.nn.dropout(nonlinearity(tf.matmul(i, self.W_sa_1) + self.b_1), self.keep_prob)
if self.one_layer_only:
return tf.matmul(o1, self.W_2_q) + self.b_q
o2 = tf.nn.dropout(nonlinearity(tf.matmul(o1, self.W_1_2) + self.b_2), self.keep_prob)
return tf.matmul(o2, self.W_2_q) + self.b_q
self.o1 = nonlinearity(tf.matmul(self.sa_learn, self.W_sa_1) + self.b_1)
self.q_learn = q_from_input(self.sa_learn)
self.y_learn = tf.placeholder(self.floatX, shape = [None, self.n_q])
self.learn_delta = tf.square(self.y_learn - self.q_learn)
self.learn_error = tf.reduce_mean(self.learn_delta)
self.max_a_time_limit = conf['max_a_time_limit']
global_step = tf.Variable(0, trainable=False)
self.learn_rate = tf.train.exponential_decay(
conf['initial_learn_rate'] / self.n_minibatch,
global_step,
conf['learn_rate_half_life'], 0.5, staircase=False)
self.learn_opt = tf.train.AdamOptimizer(self.learn_rate).minimize(self.learn_error, global_step=global_step)
self.learn_opts = []
self.feed_ys = []
for i in range(self.n_q):
feed_y = tf.placeholder(self.floatX)
self.feed_ys.append(feed_y)
learn_error = self.error_function(feed_y - self.q_learn[0,i])
learn_opt = tf.train.AdamOptimizer(self.learn_rate).minimize(learn_error, global_step=global_step)
self.learn_opts.append(learn_opt)
if self.n_a > 0:
def query_setup(n_sample):
s_query = tf.placeholder('float', shape=[n_sample, self.n_s])
a_query = unif_fanin_mat([n_sample, self.n_a], 'a_query')
min_cutoff = tf.matmul(np.ones((n_sample, 1), dtype=self.floatX), self.min_torques)
max_cutoff = tf.matmul(np.ones((n_sample, 1), dtype=self.floatX), self.max_torques)
# print "min cutoff:", self.sess.run(min_cutoff)
# print "max cutoff:", self.sess.run(max_cutoff)
a_query_clipped = tf.minimum(tf.maximum(min_cutoff, a_query), max_cutoff)
#sa_query = tf.concat(1, [s_query, a_query_clipped])
sa_query = tf.concat(1, [s_query, a_query])
q_query = q_from_input(sa_query)
q_query_mean = tf.reduce_mean(q_query)
query_opt = tf.train.AdamOptimizer(0.1)
query_grads_and_vars = query_opt.compute_gradients(q_query_mean, [a_query])
# list of tuples (gradient, variable).
query_grads_and_vars[0] = (-query_grads_and_vars[0][0], query_grads_and_vars[0][1])
apply_query_grads = query_opt.apply_gradients(query_grads_and_vars)
return s_query, a_query, a_query_clipped, sa_query, q_query, q_query_mean, apply_query_grads
self.s_query, self.a_query, self.a_query_clipped, self.sa_query, self.q_query, \
self.q_query_mean, self.apply_query_grads = query_setup(1)
self.s_query_p, self.a_query_p, self.a_query_clipped_p, self.sa_query_p, self.q_query_p, \
self.q_query_mean_p, self.apply_query_grads_p = query_setup(self.n_megabatch)
self.sym_grad_p = tf.gradients(self.q_query_mean_p, self.a_query_p)
self.sym_grad = tf.gradients(self.q_query_mean, self.a_query)
self.saver = tf.train.Saver(self.name_var_dict)
self.init_op = tf.initialize_all_variables()
self.sess.run(self.init_op)
if load_path != None:
self.load_model(load_path)
class ControlNN:
def __init__(self, conf, load_path=None):
tf_random_seed = None
nonlinearity = tf.nn.relu
self.keep_prob_train_val = 1.0
self.floatX = 'float32'
def unif_fanin_mat(shape, name):
b = np.sqrt(3 * self.keep_prob_train_val / shape[0])
initial = tf.random_uniform(shape, minval=-b, maxval=b, seed=tf_random_seed, dtype=self.floatX)
return tf.Variable(initial, name=name)
def bias(shape, name):
initial = tf.constant(0.1, shape=shape, dtype=self.floatX)
return tf.Variable(initial, name=name)
self.snopt = SNOPT_solver()
self.snopt.setOption('Major print level', 0)
#self.snopt.setOption('Major feasibility', 1.0e-6)
#self.snopt.setOption('Minor feasibility', 1.0e-6)
#self.snopt.setOption('Major optimality', 1.0e-6)
#self.snopt.setOption('Linesearch tolerance', 0.9)
#self.snopt.setOption('Major iterations', 100)
#self.snopt.setOption('Minor iterations', 50)
#self.snopt.setOption('Scale option', 0)
#self.snopt_max_count = conf['snopt_max_count']
self.snopt.setOption('Iteration limit', conf['snopt_max_count'])
self.profiler = Profiler()
self.n_s = conf['n_s']
self.n_a = conf['n_a']
self.n_sa = self.n_s + self.n_a
n_hidden = conf['n_hidden']
self.n_1 = n_hidden
self.n_2 = n_hidden
self.n_q = conf['n_q']
error_functions = [tf.square, tf.abs]
self.error_function = error_functions[conf['error_type']]
self.one_layer_only = conf['one_layer_only']
self.n_minibatch = conf['minibatch_size']
self.n_batches = conf['num_batches']
self.n_megabatch = self.n_minibatch * self.n_batches
self.max_a_min_iters = 5
self.max_abs_torque = conf['max_torque']
#self.a_tolerance = conf['a_tolerance']
self.max_torques = np.array([[self.max_abs_torque]], dtype=self.floatX)
self.max_torques_p = np.ones((self.n_megabatch,1)) * np.array([[self.max_abs_torque]], dtype=self.floatX)
self.min_torques = np.array([[-self.max_abs_torque]], dtype=self.floatX)
self.min_torques_p = np.ones((self.n_megabatch,1)) * np.array([[-self.max_abs_torque]], dtype=self.floatX)
self.set_standardizer((np.zeros(self.n_sa), np.ones(self.n_sa)),
(np.zeros(self.n_q), np.ones(self.n_q)))
self.sess = tf.Session()
self.keep_prob = tf.placeholder(self.floatX)
self.sa_learn = tf.placeholder(self.floatX, shape=[None,self.n_sa])
self.W_sa_1 = unif_fanin_mat([self.n_sa, self.n_1], 'W_sa_1')
self.b_1 = bias([self.n_1], 'b_1')
self.W_1_2 = unif_fanin_mat([self.n_1, self.n_2], 'W_1_2')
self.b_2 = bias([self.n_2], 'b_2')
self.W_2_q = unif_fanin_mat([self.n_2,self.n_q], 'W_2_q')
self.b_q = bias([self.n_q], 'b_q')
name_var_pairs = zip(['W_sa_1', 'b_1', 'b_q'],
[self.W_sa_1, self.b_1, self.b_q])
#if not self.one_layer_only:
name_var_pairs.extend(zip(['W_1_2', 'b_2', 'W_2_q'], [self.W_1_2, self.b_2, self.W_2_q]))
self.name_var_dict = {i:j for (i,j) in name_var_pairs}
# # run collapse once to set number of params
# self.collapse_params()
def q_from_input(i):
o1 = tf.nn.dropout(nonlinearity(tf.matmul(i, self.W_sa_1) + self.b_1), self.keep_prob)
if self.one_layer_only:
return tf.matmul(o1, self.W_2_q) + self.b_q
o2 = tf.nn.dropout(nonlinearity(tf.matmul(o1, self.W_1_2) + self.b_2), self.keep_prob)
return tf.matmul(o2, self.W_2_q) + self.b_q
self.o1 = nonlinearity(tf.matmul(self.sa_learn, self.W_sa_1) + self.b_1)
self.q_learn = q_from_input(self.sa_learn)
self.y_learn = tf.placeholder(self.floatX, shape = [None, self.n_q])
self.learn_delta = tf.square(self.y_learn - self.q_learn)
self.learn_error = tf.reduce_mean(self.learn_delta)
self.max_a_time_limit = conf['max_a_time_limit']
global_step = tf.Variable(0, trainable=False)
self.learn_rate = tf.train.exponential_decay(
conf['initial_learn_rate'] / self.n_minibatch,
global_step,
conf['learn_rate_half_life'], 0.5, staircase=False)
self.learn_opt = tf.train.AdamOptimizer(self.learn_rate).minimize(self.learn_error, global_step=global_step)
self.learn_opts = []
self.feed_ys = []
for i in range(self.n_q):
feed_y = tf.placeholder(self.floatX)
self.feed_ys.append(feed_y)
learn_error = self.error_function(feed_y - self.q_learn[0,i])
learn_opt = tf.train.AdamOptimizer(self.learn_rate).minimize(learn_error, global_step=global_step)
self.learn_opts.append(learn_opt)
if self.n_a > 0:
def query_setup(n_sample):
s_query = tf.placeholder('float', shape=[n_sample, self.n_s])
a_query = unif_fanin_mat([n_sample, self.n_a], 'a_query')
min_cutoff = tf.matmul(np.ones((n_sample, 1), dtype=self.floatX), self.min_torques)
max_cutoff = tf.matmul(np.ones((n_sample, 1), dtype=self.floatX), self.max_torques)
# print "min cutoff:", self.sess.run(min_cutoff)
# print "max cutoff:", self.sess.run(max_cutoff)
a_query_clipped = tf.minimum(tf.maximum(min_cutoff, a_query), max_cutoff)
#sa_query = tf.concat(1, [s_query, a_query_clipped])
sa_query = tf.concat(1, [s_query, a_query])
q_query = q_from_input(sa_query)
q_query_mean = tf.reduce_mean(q_query)
query_opt = tf.train.AdamOptimizer(0.1)
query_grads_and_vars = query_opt.compute_gradients(q_query_mean, [a_query])
# list of tuples (gradient, variable).
query_grads_and_vars[0] = (-query_grads_and_vars[0][0], query_grads_and_vars[0][1])
apply_query_grads = query_opt.apply_gradients(query_grads_and_vars)
return s_query, a_query, a_query_clipped, sa_query, q_query, q_query_mean, apply_query_grads
self.s_query, self.a_query, self.a_query_clipped, self.sa_query, self.q_query, \
self.q_query_mean, self.apply_query_grads = query_setup(1)
self.s_query_p, self.a_query_p, self.a_query_clipped_p, self.sa_query_p, self.q_query_p, \
self.q_query_mean_p, self.apply_query_grads_p = query_setup(self.n_megabatch)
self.sym_grad_p = tf.gradients(self.q_query_mean_p, self.a_query_p)
self.sym_grad = tf.gradients(self.q_query_mean, self.a_query)
self.saver = tf.train.Saver(self.name_var_dict)
self.init_op = tf.initialize_all_variables()
self.sess.run(self.init_op)
if load_path != None:
self.load_model(load_path)
def __del__(self):
self.sess.close()
def set_standardizer(self, sa_mean_std, q_mean_std):
def check_stuff(thing, l):
assert len(thing) == 2
assert thing[0].shape == thing[1].shape
assert len(thing[0].shape) == 1
assert thing[1].shape[0] == l
check_stuff(sa_mean_std, self.n_sa)
check_stuff(q_mean_std, self.n_q)
self.sa_mean_std = sa_mean_std
self.q_mean_std = q_mean_std
def print_params(self):
for (name, param) in enumerate(self.name_var_dict):
print name
print self.sess.run(param)
print
#def collapse_params(self):
# self.print_params()
# ans = []
# for (name, param) in enumerate(self.name_var_dict):
# ans.extend(self.sess.run(param).flatten().tolist())
# self.num_params = len(ans)
# print self.num_params
# return np.array(ans)
def q_from_sa(self, sa_vals):
net_q = self.sess.run(self.q_learn,
feed_dict={self.sa_learn: sa_vals, #standardize(sa_vals, self.sa_mean_std),
self.keep_prob: 1.0})
#print net_q.shape
#return unstandardize(net_q, self.q_mean_std)
return net_q
def q_query_from_s(self, s_vals):
return self.sess.run(self.q_query, feed_dict={self.s_query: s_vals[np.newaxis,:], self.keep_prob: 1.0})
def q_query_from_s_p(self, s_vals):
return self.sess.run(self.q_query_p, feed_dict={self.s_query_p: s_vals, self.keep_prob: 1.0})
def o1_from_sa(self, sa_vals):
return self.sess.run(self.o1, feed_dict={self.sa_learn: sa_vals, self.keep_prob: 1.0})
def s_const_grid(self, s, xr, n=10000):
# [[s x_1], [s x_2], ..., [s x_n]]
s = np.array(s)
xs = np.linspace(xr[0], xr[1], n)[:,np.newaxis]
return xs, np.concatenate((np.ones((n,1)) * s[np.newaxis,:], xs), 1)
def manual_max_a(self, s, xr=None):
assert self.n_a == 1
if xr == None:
xr = self.max_abs_torque * np.array([-1.,1])
xs, inputs = self.s_const_grid(s, xr)
outputs = self.q_from_sa(inputs).flatten()
print outputs
best_input = np.argmax(outputs)
return np.array([inputs[best_input][-1], outputs[best_input]])
def manual_max_a_p(self, s, xr=None):
assert self.n_a == 1
ans = []
for i in s:
ans.append(self.manual_max_a(i, xr))
return np.array(ans)
def q_from_s_discrete(self, s):
return self.q_from_sa(s)
def q_from_sa_discrete(self, s, a):
qs = self.q_from_s_discrete(s)
chosen_qs = np.array([r[i] for r,i in zip(qs, a)])
return chosen_qs[:, np.newaxis]
def get_best_a_discrete(self, s):
qs = self.q_from_s_discrete(s[np.newaxis,:])
return np.argmax(qs)
def get_best_q_discrete(self, s):
assert s.shape[1] == self.n_s
qs = self.q_from_s_discrete(s)
return np.max(qs, 1)
def get_best_a_p(self, s, is_p, num_tries, init_a=None, tolerance=0.01):
#TODO: benchmark different init methods
assert (is_p and len(s.shape) == 2 and s.shape[0] == self.n_megabatch) or (not is_p and len(s.shape) == 1)
n_batch = s.shape[0] if is_p else 1
inf = 1.0e20
obj_row = 1
x_names = np.array(['x' + str(i) for i in range(n_batch)])
F_names = np.array(['F1', 'F2'])
xlow = np.array([-self.max_abs_torque for i in range(n_batch)])
xupp = np.array([self.max_abs_torque for i in range(n_batch)])
Flow = np.array([-inf, -inf])
Fupp = np.array([inf, inf])
A = np.array([[0 for _ in range(n_batch)], [1 for _ in range(n_batch)]])
G = np.array([[2 for _ in range(n_batch)], [0 for _ in range(n_batch)]])
vs = {}
vs['count'] = 0
vs['old_a'] = None
ans_a, ans_q = None, None
def check_timeout(start_time, time_limit):
if time.time() - start_time > time_limit:
err_msg = 'error!!! max a timeout: s=%s is_p=%s num_tries=%s init_a=%s' % (s, is_p, num_tries, init_a)
print err_msg
return True
return False
def update_tolerance_conditions(a):
vs['count'] += 1
#if vs['old_a'] != None and np.max(np.abs(a - vs['old_a'])) < self.a_tolerance:
# print 'vs', vs
# return -1
#vs['old_a'] = a
if vs['count'] > self.snopt_max_count:
return -1
return 0
def inner_p(init_a, time_limit):
start_time = time.time()
if init_a == None:
init_a = self.min_torques_p + (self.max_torques_p - self.min_torques_p) * \
np.random.random((self.n_megabatch, self.n_a)) / 2.0
def grad(a):
g = self.sess.run(self.sym_grad_p, feed_dict={self.s_query_p: s, self.a_query_p: a[:,np.newaxis],
self.keep_prob: 1.0})
ret_grad = -g[0].flatten()
return ret_grad
def objFG(status,a,needF,needG,cu,iu,ru):
F = np.array([-self.sess.run(self.q_query_mean_p, feed_dict={self.s_query_p: s,
self.a_query_p: a[:,np.newaxis], self.keep_prob: 1.0}), 0])
G = grad(a)
#status = update_tolerance_conditions(a)
return status, F, G
self.snopt.snopta(n=n_batch, nF=2, usrfun=objFG, x0=init_a, xlow=xlow, xupp=xupp,
Flow=Flow, Fupp=Fupp, ObjRow=obj_row, A=A, G=G, xnames=x_names, Fnames=F_names)
res_x = self.snopt.x
q_final = self.sess.run(self.q_query_p, feed_dict={self.s_query_p: s,
self.a_query_p: res_x, self.keep_prob: 1.0})
return res_x, q_final
def inner(init_a, time_limit):
start_time = time.time()
if init_a == None:
#init_a = np.zeros([1, self.n_a])
init_a = self.min_torques + (self.max_torques - self.min_torques) * \
np.random.random((1, self.n_a))
def grad(a):
g = self.sess.run(self.sym_grad, feed_dict={self.s_query: s[np.newaxis,:],
self.a_query: a[:,np.newaxis], self.keep_prob: 1.0})
ret_grad = -g[0].flatten()
return ret_grad
def objFG(status,a,needF,needG,cu,iu,ru):
F = np.array([-self.sess.run(self.q_query_mean, feed_dict={self.s_query: s[np.newaxis,:],
self.a_query: a[:,np.newaxis], self.keep_prob: 1.0}), 0])
G = grad(a)
#status = update_tolerance_conditions(a)
return status, F, G
self.snopt.snopta(n=n_batch, nF=2, usrfun=objFG, x0=init_a, xlow=xlow, xupp=xupp,
Flow=Flow, Fupp=Fupp, ObjRow=obj_row, A=A, G=G)#, xnames=x_names, Fnames=F_names)
res_x = self.snopt.x
print init_a, res_x
q_final = self.sess.run(self.q_query, feed_dict={self.s_query: s[np.newaxis,:],
self.a_query: res_x, self.keep_prob: 1.0})
return res_x, q_final
inner_function = inner_p if is_p else inner
iter_time_limit = self.max_a_time_limit / num_tries
for i in range(num_tries):
a, q = inner_function(init_a, iter_time_limit) if i == 0 else inner_function(None, iter_time_limit)
if i == 0:
ans_a, ans_q = a, q
continue
for i in range(len(q)):
if q[i] > ans_q[i]:
ans_q[i] = q[i]
ans_a[i] = a[i]
return ans_a, ans_q
def get_learn_rate(self):
return self.sess.run(self.learn_rate)
def learn_delta_q(self, sa_vals, y_vals):
return self.sess.run(self.learn_delta,
feed_dict={self.sa_learn: sa_vals, self.y_learn: y_vals, self.keep_prob: 1.0})
def mse_q(self, sa_vals, y_vals):
return self.sess.run(self.learn_error,
feed_dict={self.sa_learn: sa_vals, self.y_learn: y_vals, self.keep_prob: 1.0})
def train(self, sa_vals, y_vals):
self.sess.run(self.learn_opt, feed_dict={self.y_learn: y_vals,
self.sa_learn: sa_vals, self.keep_prob: self.keep_prob_train_val})
#print 'learn_rate', self.sess.run(self.learn_rate)
def train_discrete(self, s_vals, a_vals, y_vals):
for (s, a, y) in zip(s_vals, a_vals, y_vals):
self.sess.run(self.learn_opts[a], feed_dict={self.sa_learn: s[np.newaxis,:],
self.feed_ys[a]: y, self.keep_prob: 1.0})
def save_model(self, save_path):
self.saver.save(self.sess, save_path)
def load_model(self, load_path):
print 'loading model from', load_path
self.saver.restore(self.sess,
'/Users/jeffreyyan/drake-distro/drake/examples/NN/' + load_path)