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sample_acceleration.py
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import numpy as np
import casadi as cs
import adam_model
import parser
import copy
import random
import tqdm
import pickle
import datetime
class MinAccProblem:
""" Define OCP problem and solver (IpOpt) """
def __init__(self, model, joint): # joint 0<= joint <= nq : joint on which maximize acceleration, while keeping zero accleration to the other
self.params = model.params
self.model = model
self.nq = model.nq
self.joint = joint
opti = cs.Opti()
x_init = opti.parameter(model.nx)
# Define decision variables
X, U = [], []
X += [opti.variable(model.nx)] # x_0
X += [opti.variable(model.nx)] # x_next
opti.subject_to(opti.bounded(model.x_min, X[-1], model.x_max))
U += [opti.variable(model.nu)] # acceleration to maximize
opti.subject_to(X[0] == x_init)
self.cost = U[-1][joint]
# Dynamics constraint
opti.subject_to(X[1] == model.f_fun(X[0], U[0]))
# Torque constraint
opti.subject_to(opti.bounded(model.tau_min, model.tau_fun(X[0], U[0]), model.tau_max))
for i in range(self.model.nq):
if i!= joint:
opti.subject_to(U[-1][i]==0)
self.X = X
self.U = U
self.x_init = x_init
self.additionalSetting()
opti.minimize(self.cost)
self.opti = opti
def additionalSetting(self):
pass
def instantiateProblem(self):
opti = self.opti
opts = {
'ipopt.print_level': 0,
'print_time': 0,
'ipopt.tol': 1e-6,
'ipopt.constr_viol_tol': 1e-6,
'ipopt.compl_inf_tol': 1e-6,
#'ipopt.hessian_approximation': 'limited-memory',
# 'detect_simple_bounds': 'yes',
'ipopt.max_iter': self.params.nlp_max_iter,
#'ipopt.linear_solver': 'ma57',
'ipopt.sb': 'yes'
}
opti.solver('ipopt', opts)
return opti
class MaxAccProblem(MinAccProblem):
""" Define OCP problem and solver (IpOpt) """
def __init__(self, model, joint): # joint 0<= joint <= nq : joint on which maximize acceleration, while keeping zero accleration to the other
self.params = model.params
self.model = model
self.nq = model.nq
self.joint = joint
opti = cs.Opti()
x_init = opti.parameter(model.nx)
# Define decision variables
X, U = [], []
X += [opti.variable(model.nx)] # x_0
X += [opti.variable(model.nx)] # x_next
opti.subject_to(opti.bounded(model.x_min, X[-1], model.x_max))
U += [opti.variable(model.nu)] # acceleration to maximize
opti.subject_to(X[0] == x_init)
self.cost = -U[-1][joint]
# Dynamics constraint
opti.subject_to(X[1] == model.f_fun(X[0], U[0]))
# Torque constraint
opti.subject_to(opti.bounded(model.tau_min, model.tau_fun(X[0], U[0]), model.tau_max))
for i in range(self.model.nq):
if i!= joint:
opti.subject_to(U[-1][i]==0)
self.X = X
self.U = U
self.x_init = x_init
self.additionalSetting()
opti.minimize(self.cost)
self.opti = opti
def additionalSetting(self):
self.cost = - self.U[-1][self.joint]
if __name__ == "__main__":
now = datetime.datetime.now()
# params = parser.Parameters('z1')
# robot = adam_model.AdamModel(params,n_dofs=4)
params = parser.Parameters('fr3')
robot = adam_model.AdamModel(params,n_dofs=6)
if robot.params.urdf_name == 'fr3':
robot.tau_max = np.array([17,87,8.7,34.8,2.4,4.8])
robot.tau_min = -np.array([17,87,8.7,34.8,2.4,4.8])
n_samples = 10000 # samples for each joint
acc_max = [[] for _ in range(robot.nq)]
acc_min = [[] for _ in range(robot.nq)]
acc_min_x = [[] for _ in range(robot.nq)]
acc_max_x = [[] for _ in range(robot.nq)]
progress_bar = tqdm.tqdm(total=n_samples*robot.nq*2, desc='Sampling started')
for k in range(robot.nq):
i=0
while i < n_samples:
if i == 0:
print('min acc')
x0 = np.array((robot.x_max-robot.x_min)*np.random.random_sample((robot.nx,)) + robot.x_min*np.ones((robot.nx,)))
min_problem = MinAccProblem(robot,k)
min_solver = min_problem.instantiateProblem()
min_solver.set_value(min_problem.x_init,x0)
try:
sol = min_solver.solve()
acc_min[k].append(sol.value(min_problem.U[-1][k]))
#print(sol.value(min_problem.U[-1][k]))
ddx_min = np.array(robot.jac(np.eye(4),sol.value(min_problem.X[0][:robot.nq]))[:3,6:]@sol.value(min_problem.U[-1])) + \
robot.jac_dot(np.eye(4),sol.value(min_problem.X[0][:robot.nq]),np.zeros(6),sol.value(min_problem.X[0][robot.nq:]))[:3,6:]@sol.value(min_problem.X[0][robot.nq:])
acc_min_x[k].append(copy.copy(ddx_min))
progress_bar.update(1)
i+=1
except:
print('failed')
i=0
while i < n_samples:
if i == 0:
print('max acc')
x0 = np.array((robot.x_max-robot.x_min)*np.random.random_sample((robot.nx,)) + robot.x_min*np.ones((robot.nx,)))
max_problem = MaxAccProblem(robot,k)
max_solver = max_problem.instantiateProblem()
max_solver.set_value(max_problem.x_init,x0)
try:
sol = max_solver.solve()
acc_max[k].append(sol.value(max_problem.U[-1][k]))
#print(sol.value(max_problem.U[-1][k]))
ddx_max = np.array(robot.jac(np.eye(4),sol.value(max_problem.X[0][:robot.nq]))[:3,6:]@sol.value(max_problem.U[-1])) + \
robot.jac_dot(np.eye(4),sol.value(max_problem.X[0][:robot.nq]),np.zeros(6),sol.value(max_problem.X[0][robot.nq:]))[:3,6:]@sol.value(max_problem.X[0][robot.nq:])
acc_max_x[k].append(copy.copy(ddx_max))
progress_bar.update(1)
i+=1
except:
print('failed')
saving_date = str(datetime.datetime.now())
with open(saving_date+'min.pkl', 'wb') as file:
pickle.dump(acc_min, file)
with open(saving_date+'max.pkl', 'wb') as file:
pickle.dump(acc_max, file)
with open(saving_date+'ddx_min.pkl', 'wb') as file:
pickle.dump(acc_min_x, file)
with open(saving_date+'ddx_max.pkl', 'wb') as file:
pickle.dump(acc_max_x, file)
progress_bar.close()