forked from hoergems/LQG
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mpc.py
executable file
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mpc.py
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import sys
import argparse
import time
import numpy as np
import os
import glob
import scipy
from serializer import Serializer
from libutil import *
import logging
from librobot import v_string, Robot
from util_py import check_positive_definite, get_goal_states, copyToTmp
from simulator import Simulator
from path_evaluator import PathEvaluator
from path_planning_interface import PathPlanningInterface
from libobstacle import Obstacle, Terrain
import warnings
class MPC:
def __init__(self, plot):
self.abs_path = os.path.dirname(os.path.abspath(__file__))
""" Reading the config """
warnings.filterwarnings("ignore")
self.init_serializer()
config = self.serializer.read_config("config_mpc.yaml", path=self.abs_path)
self.set_params(config)
if self.seed < 0:
"""
Generate a random seed that will be stored
"""
self.seed = np.random.randint(0, 42949672)
np.random.seed(self.seed)
logging_level = logging.WARN
if config['verbose']:
logging_level = logging.DEBUG
logging.basicConfig(format='%(levelname)s: %(message)s', level=logging_level)
np.set_printoptions(precision=16)
dir = self.abs_path + "/stats/mpc"
tmp_dir = self.abs_path + "/tmp/mpc"
self.utils = Utils()
if not self.init_robot(self.robot_file):
logging.error("MPC: Couldn't initialize robot")
return
if not self.setup_scene(self.environment_file, self.robot):
return
#self.run_viewer(self.robot_file, self.environment_file)
self.clear_stats(dir)
logging.info("Start up simulator")
sim = Simulator()
path_evaluator = PathEvaluator()
path_planner = PathPlanningInterface()
logging.info("MPC: Generating goal states...")
goal_states = get_goal_states("mpc",
self.abs_path,
self.serializer,
self.obstacles,
self.robot,
self.max_velocity,
self.delta_t,
self.start_state,
self.goal_position,
self.goal_radius,
self.planning_algortihm,
self.path_timeout,
self.num_generated_goal_states,
self.continuous_collision,
self.environment_file,
self.num_cores)
if len(goal_states) == 0:
logging.error("MPC: Couldn't generate any goal states. Problem seems to be infeasible")
return
logging.info("MPC: Generated " + str(len(goal_states)) + " goal states")
sim.setup_reward_function(self.discount_factor, self.step_penalty, self.illegal_move_penalty, self.exit_reward)
path_planner.setup(self.robot,
self.obstacles,
self.max_velocity,
self.delta_t,
self.use_linear_path,
self.planning_algortihm,
self.path_timeout,
self.continuous_collision,
self.num_cores)
if self.dynamic_problem:
path_planner.setup_dynamic_problem(self.robot_file,
self.environment_file,
self.simulation_step_size,
self.num_control_samples,
self.min_control_duration,
self.max_control_duration,
self.add_intermediate_states,
self.rrt_goal_bias,
self.control_sampler)
A, H, B, V, W, C, D, M_base, N_base = self.problem_setup(self.delta_t, self.robot_dof)
time_to_generate_paths = 0.0
if check_positive_definite([C, D]):
m_covs = None
if self.inc_covariance == "process":
m_covs = np.linspace(self.min_process_covariance,
self.max_process_covariance,
self.covariance_steps)
elif self.inc_covariance == "observation":
m_covs = np.linspace(self.min_observation_covariance,
self.max_observation_covariance,
self.covariance_steps)
for j in xrange(len(m_covs)):
M = None
N = None
if self.inc_covariance == "process":
""" The process noise covariance matrix """
M = self.calc_covariance_value(self.robot, m_covs[j], M_base)
#M = m_covs[j] * M_base
""" The observation error covariance matrix """
N = self.calc_covariance_value(self.robot,
self.min_observation_covariance,
N_base,
covariance_type='observation')
elif self.inc_covariance == "observation":
M = self.calc_covariance_value(self.robot,
self.min_process_covariance,
M_base)
N = self.calc_covariance_value(self.robot,
m_covs[j],
N_base,
covariance_type='observation')
P_t = np.array([[0.0 for k in xrange(2 * self.robot_dof)] for l in xrange(2 * self.robot_dof)])
path_planner.setup_path_evaluator(A, B, C, D, H, M, N, V, W,
self.robot,
self.sample_size,
self.obstacles,
self.goal_position,
self.goal_radius,
self.robot_file,
self.environment_file)
sim.setup_problem(A, B, C, D, H, V, W, M, N,
self.robot,
self.enforce_control_constraints,
self.obstacles,
self.goal_position,
self.goal_radius,
self.max_velocity,
self.show_viewer_simulation,
self.robot_file,
self.environment_file)
sim.set_stop_when_colliding(self.replan_when_colliding)
if self.dynamic_problem:
path_evaluator.setup_dynamic_problem()
sim.setup_dynamic_problem(self.simulation_step_size)
path_planner.setup_reward_function(self.step_penalty, self.exit_reward, self.illegal_move_penalty, self.discount_factor)
mean_number_planning_steps = 0.0
number_of_steps = 0.0
mean_planning_time = 0.0
num_generated_paths_run = 0.0
successful_runs = 0
num_collisions = 0.0
linearization_error = 0.0
final_states= []
rewards_cov = []
for k in xrange(self.num_simulation_runs):
print "MPC: Run " + str(k + 1)
self.serializer.write_line("log.log", tmp_dir, "RUN #" + str(k + 1) + " \n")
current_step = 0
x_true = self.start_state
x_estimate = self.start_state
x_tilde_linear = np.array([0.0 for i in xrange(2 * self.robot_dof)])
P_t = np.array([[0.0 for i in xrange(2 * self.robot_dof)] for i in xrange(2 * self.robot_dof)])
deviation_covariance = np.array([[0.0 for i in xrange(2 * self.robot_dof)] for i in xrange(2 * self.robot_dof)])
estimated_deviation_covariance = np.array([[0.0 for i in xrange(2 * self.robot_dof)] for i in xrange(2 * self.robot_dof)])
total_reward = 0.0
terminal = False
while current_step < self.max_num_steps and not terminal:
path_planner.set_start_and_goal(x_estimate, goal_states, self.goal_position, self.goal_radius)
t0 = time.time()
(xs,
us,
zs,
control_durations,
num_generated_paths,
best_val,
state_covariances,
deviation_covariances,
estimated_deviation_covariances,
mean_gen_times,
mean_eval_times,
total_gen_times,
total_eval_times) = path_planner.plan_and_evaluate_paths(self.num_paths,
0,
current_step,
self.evaluation_horizon,
P_t,
deviation_covariance,
estimated_deviation_covariance,
self.timeout)
mean_planning_time += time.time() - t0
mean_number_planning_steps += 1.0
num_generated_paths_run += num_generated_paths
if len(xs) == 0:
logging.error("MPC: Couldn't find any paths from start state" +
str(x_estimate) +
" to goal states")
final_states.append(x_true)
#total_reward = np.array([-self.illegal_move_penalty])[0]
current_step += 1
break
x_tilde = np.array([0.0 for i in xrange(2 * self.robot_dof)])
n_steps = self.num_execution_steps
if n_steps > len(xs) - 1:
n_steps = len(xs) - 1
if current_step + n_steps > self.max_num_steps:
n_steps = self.max_num_steps - current_step
(x_true,
x_tilde,
x_tilde_linear,
x_estimate,
P_t,
current_step,
total_reward,
terminal,
estimated_s,
estimated_c,
history_entries) = sim.simulate_n_steps(xs, us, zs,
control_durations,
x_true,
x_tilde,
x_tilde_linear,
x_estimate,
P_t,
total_reward,
current_step,
n_steps,
0.0,
0.0,
max_num_steps=self.max_num_steps)
#print "len(hist) " + str(len(history_entries))
#print "len(deviation_covariances) " + str(len(deviation_covariances))
#print "len(estimated_deviation_covariances) " + str(len(estimated_deviation_covariances))
try:
deviation_covariance = deviation_covariances[len(history_entries) - 1]
estimated_deviation_covariance = estimated_deviation_covariances[len(history_entries) - 1]
except:
print "what: len(deviation_covariances) " + str(len(deviation_covariances))
print "len(history_entries) " + str(len(history_entries))
print "len(xs) " + str(len(xs))
if (current_step == self.max_num_steps) or terminal:
final_states.append(history_entries[-1].x_true)
if terminal:
successful_runs += 1
history_entries[0].set_replanning(True)
for l in xrange(len(history_entries)):
try:
history_entries[l].set_estimated_covariance(state_covariances[l])
except:
print "l " + str(l)
print "len(state_covariances) " + str(len(state_covariances))
history_entries[l].serialize(tmp_dir, "log.log")
if history_entries[l].collided:
num_collisions += 1
linearization_error += history_entries[l].linearization_error
#logging.warn("MPC: Execution finished. True state is " + str(x_true))
#logging.warn("MPC: Estimated state is " + str(x_estimate))
logging.warn("MPC: Executed " + str(current_step) + " steps")
logging.warn("MPC: terminal " + str(terminal))
if terminal:
logging.info("MPC: Final state: " + str(x_true))
rewards_cov.append(total_reward)
self.serializer.write_line("log.log",
tmp_dir,
"Reward: " + str(total_reward) + " \n")
self.serializer.write_line("log.log",
tmp_dir,
"\n")
number_of_steps += current_step
mean_planning_time_per_step = mean_planning_time / mean_number_planning_steps
mean_planning_time /= self.num_simulation_runs
""" Calculate the distance to goal area
"""
ee_position_distances = []
for state in final_states:
joint_angles = v_double()
joint_angles[:] = [state[y] for y in xrange(len(state) / 2)]
ee_position = v_double()
self.robot.getEndEffectorPosition(joint_angles, ee_position)
ee_position = np.array([ee_position[y] for y in xrange(len(ee_position))])
dist = np.linalg.norm(np.array(self.goal_position - ee_position))
if dist < self.goal_radius:
dist = 0.0
ee_position_distances.append(dist)
logging.info("MPC: Done. total_reward is " + str(total_reward))
try:
n, min_max, mean_distance_to_goal, var, skew, kurt = scipy.stats.describe(np.array(ee_position_distances))
except:
print ee_position_distances
self.serializer.write_line("log.log", tmp_dir, "################################# \n")
self.serializer.write_line("log.log",
tmp_dir,
"inc_covariance: " + str(self.inc_covariance) + "\n")
if self.inc_covariance == "process":
self.serializer.write_line("log.log",
tmp_dir,
"Process covariance: " + str(m_covs[j]) + " \n")
self.serializer.write_line("log.log",
tmp_dir,
"Observation covariance: " + str(self.min_observation_covariance) + " \n")
elif self.inc_covariance == "observation":
self.serializer.write_line("log.log",
tmp_dir,
"Process covariance: " + str(self.min_process_covariance) + " \n")
self.serializer.write_line("log.log",
tmp_dir,
"Observation covariance: " + str(m_covs[j]) + " \n")
mean_linearization_error = linearization_error / number_of_steps
number_of_steps /= self.num_simulation_runs
self.serializer.write_line("log.log", tmp_dir, "Mean number of steps: " + str(number_of_steps) + " \n")
self.serializer.write_line("log.log", tmp_dir, "Mean num collisions per run: " + str(float(num_collisions) / float(self.num_simulation_runs)) + " \n")
self.serializer.write_line("log.log",
tmp_dir,
"Average distance to goal area: " + str(mean_distance_to_goal) + " \n")
self.serializer.write_line("log.log", tmp_dir, "Num successes: " + str(successful_runs) + " \n")
print "succ " + str((100.0 / self.num_simulation_runs) * successful_runs)
self.serializer.write_line("log.log", tmp_dir, "Percentage of successful runs: " + str((100.0 / self.num_simulation_runs) * successful_runs) + " \n")
self.serializer.write_line("log.log", tmp_dir, "Mean planning time per run: " + str(mean_planning_time) + " \n")
self.serializer.write_line("log.log", tmp_dir, "Mean planning time per planning step: " + str(mean_planning_time_per_step) + " \n")
n, min_max, mean, var, skew, kurt = scipy.stats.describe(np.array(rewards_cov))
print "mean_rewards " + str(mean)
#plt.plot_histogram_from_data(rewards_cov)
#sleep
self.serializer.write_line("log.log", tmp_dir, "Mean rewards: " + str(mean) + " \n")
self.serializer.write_line("log.log", tmp_dir, "Reward variance: " + str(var) + " \n")
self.serializer.write_line("log.log", tmp_dir, "Mean linearisation error: " + str(mean_linearization_error) + " \n")
self.serializer.write_line("log.log",
tmp_dir,
"Reward standard deviation: " + str(np.sqrt(var)) + " \n")
self.serializer.write_line("log.log", tmp_dir, "Seed: " + str(self.seed) + " \n")
cmd = "mv " + tmp_dir + "/log.log " + dir + "/log_mpc_" + str(m_covs[j]) + ".log"
os.system(cmd)
cmd = "cp " + self.abs_path + "/config_mpc.yaml " + dir
os.system(cmd)
if not os.path.exists(dir + "/environment"):
os.makedirs(dir + "/environment")
cmd = "cp " + self.abs_path + "/" + str(self.environment_file) + " " + str(dir) + "/environment"
os.system(cmd)
if not os.path.exists(dir + "/model"):
os.makedirs(dir + "/model")
cmd = "cp " + self.abs_path + "/" + self.robot_file + " " + dir + "/model"
os.system(cmd)
print "Done."
def init_robot(self, urdf_model_file):
self.robot = Robot(self.abs_path + "/" + urdf_model_file)
self.robot.enforceConstraints(self.enforce_constraints)
self.robot.setGravityConstant(self.gravity_constant)
self.robot.setAccelerationLimit(self.acceleration_limit)
""" Setup operations """
self.robot_dof = self.robot.getDOF()
if len(self.start_state) != 2 * self.robot_dof:
logging.error("MPC: Start state dimension doesn't fit to the robot state space dimension")
return False
return True
def run_viewer(self, model_file, env_file):
show_viewer = True
rot = v_double()
trans = v_double()
rot[:] = [-1.0, 0.0, 0.0, 0.0]
trans[:] = [0.0, 0.0, 3.0]
if show_viewer:
self.robot.setViewerBackgroundColor(0.6, 0.8, 0.6)
self.robot.setViewerSize(1280, 768)
self.robot.setupViewer(model_file, env_file)
fx = 0.0
fy = 0.0
fz = 0.0
f_roll = 0.0
f_pitch = 0.0
f_yaw = 0.0
x = [0.0 for i in xrange(2 * self.robot_dof)]
x = [0.8110760662014179,
-0.2416850600576381,
1.5922944779127346,
5.1412306972285471,
-10.0,
2.5101115097604483]
integration_times = []
collision_check_times1 = []
collision_check_times2 = []
y = 0
while True:
#u_in = [3.0, 1.5, 0.0, 0.0, 0.0, 0.0]
u_in = [0.0 for i in xrange(self.robot_dof)]
#u_in[0] = 150.0
#u_in[1] = 70.0
current_state = v_double()
current_state[:] = x
control = v_double()
control[:] = u_in
control_error = v_double()
ce = [0.0 for i in xrange(self.robot_dof)]
control_error[:] = ce
result = v_double()
t0 = time.time()
self.robot.propagate(current_state,
control,
control_error,
self.simulation_step_size,
0.03,
result)
t = time.time() - t0
integration_times.append(t)
if y == 10000:
t_sum = sum(integration_times)
t_mean = t_sum / len(integration_times)
print "mean integration times: " + str(t_mean)
t_sum = sum(collision_check_times1)
t_mean = t_sum / len(collision_check_times1)
print "mean collision check times old " + str(t_mean)
t_mean = sum(collision_check_times2) / len(collision_check_times2)
print "mean collision check times new " + str(t_mean)
sleep
ja_start = v_double()
ja_start[:] = [current_state[i] for i in xrange(len(current_state) / 2)]
collision_objects_start = self.robot.createRobotCollisionObjects(ja_start)
joint_angles = v_double()
joint_angles[:] = [result[i] for i in xrange(len(result) / 2)]
collision_objects_goal = self.robot.createRobotCollisionObjects(joint_angles)
#print "ee_velocity " + str([ee_velocity[i] for i in xrange(len(ee_velocity))])
t_coll_check1 = time.time()
t2 = time.time() - t_coll_check1
collision_check_times2.append(t2)
'''
Get the end effector position
'''
#ee_position = v_double()
#self.robot.getEndEffectorPosition(joint_angles, ee_position)
#ee_collision_objects = self.robot.createEndEffectorCollisionObject(joint_angles)
in_collision = False
t_c = time.time()
for o in self.obstacles:
if o.inCollisionDiscrete(collision_objects_goal):
in_collision = True
break
'''for i in xrange(len(collision_objects_start)):
if o.inCollisionContinuous([collision_objects_start[i], collision_objects_goal[i]]):
in_collision = True
break'''
if in_collision:
print "COLLISION"
t = time.time() - t_c
collision_check_times1.append(t)
x = np.array([result[i] for i in xrange(len(result))])
cjvals = v_double()
cjvels = v_double()
cjvals_arr = [x[i] for i in xrange(len(x) / 2)]
cjvels_arr = [x[i] for i in xrange(len(x) / 2, len(x))]
cjvals[:] = cjvals_arr
cjvels[:] = cjvels_arr
particle_joint_values = v2_double()
if show_viewer:
self.robot.updateViewerValues(cjvals,
cjvels,
particle_joint_values,
particle_joint_values)
time.sleep(0.03)
y += 1
print y
def setup_scene(self,
environment_file,
robot):
""" Load the obstacles """
self.obstacles = self.utils.loadObstaclesXML(self.abs_path + "/" + environment_file)
""" Load the goal area """
goal_area = v_double()
self.utils.loadGoalArea(self.abs_path + "/" + environment_file, goal_area)
if len(goal_area) == 0:
print "ERROR: Your environment file doesn't define a goal area"
return False
self.goal_position = [goal_area[i] for i in xrange(0, 3)]
self.goal_radius = goal_area[3]
return True
def init_serializer(self):
self.serializer = Serializer()
self.serializer.create_temp_dir(self.abs_path, "mpc")
def clear_stats(self, dir):
if os.path.isdir(dir):
cmd = "rm -rf " + dir + "/*"
os.system(cmd)
else:
os.makedirs(dir)
def get_average_distance_to_goal_area(self, goal_position, goal_radius, cartesian_coords):
avg_dist = 0.0
goal_pos = np.array(goal_position)
for i in xrange(len(cartesian_coords)):
cart = np.array(cartesian_coords[i])
dist = np.linalg.norm(goal_pos - cart)
if dist < goal_radius:
dist = 0.0
avg_dist += dist
if avg_dist == 0.0:
return avg_dist
return np.asscalar(avg_dist) / len(cartesian_coords)
def calc_covariance_value(self, robot, error, covariance_matrix, covariance_type='process'):
active_joints = v_string()
robot.getActiveJoints(active_joints)
if covariance_type == 'process':
if self.dynamic_problem:
torque_limits = v_double()
robot.getJointTorqueLimits(active_joints, torque_limits)
torque_limits = [torque_limits[i] for i in xrange(len(torque_limits))]
for i in xrange(len(torque_limits)):
torque_range = 2.0 * torque_limits[i]
covariance_matrix[i, i] = np.square((torque_range / 100.0) * error)
else:
for i in xrange(self.robot_dof):
covariance_matrix[i, i] = np.square((self.max_velocity / 100.0) * error)
else:
lower_position_limits = v_double()
upper_position_limits = v_double()
velocity_limits = v_double()
robot.getJointLowerPositionLimits(active_joints, lower_position_limits)
robot.getJointUpperPositionLimits(active_joints, upper_position_limits)
robot.getJointVelocityLimits(active_joints, velocity_limits)
for i in xrange(self.robot_dof):
position_range = upper_position_limits[i] - lower_position_limits[i]
covariance_matrix[i, i] = np.square((position_range / 100.0) * error)
velocity_range = 2.0 * velocity_limits[i]
covariance_matrix[i + self.robot_dof, i + self.robot_dof] = np.square((velocity_range / 100.0) * error)
return covariance_matrix
def problem_setup(self, delta_t, num_links):
A = np.identity(num_links * 2)
H = np.identity(num_links * 2)
W = np.identity(num_links * 2)
C = self.path_deviation_cost * np.identity(num_links * 2)
'''B = delta_t * np.identity(num_links * 2)
V = np.identity(num_links * 2)
D = self.control_deviation_cost * np.identity(num_links * 2)
M_base = np.identity(2 * self.robot_dof)
N_base = np.identity(2 * self.robot_dof)'''
B = delta_t * np.vstack((np.identity(num_links),
np.zeros((num_links, num_links))))
V = np.vstack((np.identity(num_links),
np.zeros((num_links, num_links))))
D = self.control_deviation_cost * np.identity(num_links)
M_base = np.identity(self.robot_dof)
N_base = np.identity(2 * self.robot_dof)
return A, H, B, V, W, C, D, M_base, N_base
def set_params(self, config):
self.num_paths = config['num_generated_paths']
self.use_linear_path = config['use_linear_path']
self.max_velocity = config['max_velocity']
self.delta_t = 1.0 / config['control_rate']
self.start_state = config['start_state']
self.num_simulation_runs = config['num_simulation_runs']
self.num_bins = config['num_bins']
self.min_process_covariance = config['min_process_covariance']
self.max_process_covariance = config['max_process_covariance']
self.covariance_steps = config['covariance_steps']
self.min_observation_covariance = config['min_observation_covariance']
self.max_observation_covariance = config['max_observation_covariance']
self.discount_factor = config['discount_factor']
self.illegal_move_penalty = config['illegal_move_penalty']
self.step_penalty = config['step_penalty']
self.exit_reward = config['exit_reward']
self.stop_when_terminal = config['stop_when_terminal']
self.enforce_constraints = config['enforce_constraints']
self.enforce_control_constraints = config['enforce_control_constraints']
self.sample_size = config['sample_size']
self.plot_paths = config['plot_paths']
self.planning_algortihm = config['planning_algorithm']
self.dynamic_problem = config['dynamic_problem']
self.simulation_step_size = config['simulation_step_size']
self.path_timeout = config['path_timeout']
self.continuous_collision = config['continuous_collision_check']
self.show_viewer_evaluation = config['show_viewer_evaluation']
self.show_viewer_simulation = config['show_viewer_simulation']
self.path_deviation_cost = config['path_deviation_cost']
self.control_deviation_cost = config['control_deviation_cost']
self.num_control_samples = config['num_control_samples']
self.min_control_duration = config['min_control_duration']
self.max_control_duration = config['max_control_duration']
self.inc_covariance = config['inc_covariance']
self.add_intermediate_states = config['add_intermediate_states']
self.gravity_constant = config['gravity']
self.num_generated_goal_states = config['num_generated_goal_states']
self.robot_file = config['robot_file']
self.environment_file = config['environment_file']
self.rrt_goal_bias = config['rrt_goal_bias']
self.control_sampler = config['control_sampler']
self.max_num_steps = config['max_num_steps']
self.evaluation_horizon = config['horizon']
self.timeout = config['timeout']
self.seed = config['seed']
self.num_cores = config['num_cores']
self.replan_when_colliding = config['replan_when_colliding']
self.acceleration_limit = config['acceleration_limit']
"""
The number of steps a path is being executed before a new path gets calculated
"""
self.num_execution_steps = config['num_execution_steps']
if __name__ == "__main__":
if len(sys.argv) > 1:
if "plot" in sys.argv[1]:
MPC(True)
sys.exit()
logging.error("Unrecognized command line argument: " + str(sys.argv[1]))
sys.exit()
MPC(False)