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utils.py
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utils.py
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import os
import numpy as np
import math
import parameters
import draw
import argparse
from algorithms.DR import Robot, Robot_true
from algorithms.BDA import Robot_BDA_EKF
from algorithms.DMV import Robot_DMV
from algorithms.CU import Robot_BDA_EKF_CU, Robot_CI_CU
from algorithms.DCL_GS import Robot_GS_LRHKF
R_0 = parameters.R_0
R_1 = parameters.R_1
MU_RANGE_BEARING, MU_POSE = parameters.MU_RANGE_BEARING, parameters.MU_POSE
BIAS_SIGMA_RANGE_BEARING, BIAS_SIGMA_POSE = parameters.BIAS_SIGMA_RANGE_BEARING, parameters.BIAS_SIGMA_POSE
# comm_fail_prob = parameters.comm_fail_prob
COMM_RATE = parameters.COMM_RATE
SIGMA_V_INPUT = parameters.SIGMA_V_INPUT
SIGMA_OMEGA_INPUT = parameters.SIGMA_OMEGA_INPUT
E_V = parameters.E_V
E_OMEGA = parameters.E_OMEGA
V_MAX = parameters.V_MAX
V_MIN = parameters.V_MIN
OMEGA_MAX = parameters.OMEGA_MAX
OMEGA_MIN = parameters.OMEGA_MIN
LANDMARK_POS = parameters.LANDMARK_POS
DELTA_T = parameters.DELTA_T
Q_BACKUP = parameters.Q
OMEGA_MAX_BACKUP, OMEGA_MIN_BACKUP = OMEGA_MAX, OMEGA_MIN
def update_para():
global OMEGA_MAX, OMEGA_MIN, SIGMA_OMEGA_INPUT
OMEGA_MAX = parameters.OMEGA_MAX
OMEGA_MIN = parameters.OMEGA_MIN
SIGMA_OMEGA_INPUT = parameters.SIGMA_OMEGA_INPUT
def draw_RMSE(ax, list_times, list_RMSE, str_color, str_label, bool_label):
"""
draw RMSE(in mission 4/5)
:param ax: axis
:param list_times: time list
:param list_RMSE: RMSE list
:param str_color: color
:param str_label: label
:param bool_label: whether to show label
"""
if bool_label:
ax.plot(list_times, list_RMSE, '-', linewidth=2,
c=str_color, label=str_label)
else:
ax.plot(list_times, list_RMSE, '-', linewidth=2, c=str_color)
def storage(algs_robots, dr_robots, cen_robots=[]):
"""
store the data
:param algs_robots: list of algorithms
:param dr_robots: list of DR
:param cen_robots: list of centralized CL
"""
RMSE, ANEES, M = {}, {}, {}
for type, alg_robots in algs_robots.items():
RMSE[type] = []
ANEES[type] = []
for robot_i in alg_robots:
RMSE[type].append(robot_i.RMSE_list)
ANEES[type].append(robot_i.ANEES_list)
if type in {28}:
M[type] = []
for robot_i in alg_robots:
M[type].append(robot_i.psi_y_time)
if dr_robots is not None:
RMSE[-1] = []
ANEES[-1] = []
for robot_i in dr_robots:
RMSE[-1].append(robot_i.RMSE_list)
ANEES[-1].append(robot_i.ANEES_list)
# Pay attention to the format (Transpose)
# RMSE[-2] = cen_robots.RMSE_list
# ANEES[-2] = cen_robots.ANEES_list
return RMSE, ANEES, M
def run(init_X, numbers=10000, flag=0, types=[-1, 0], comm_fail_prob=0, meas_bia_prob=0, Seed=0, mission=0):
'''
Parameters
----
init_X: numpy.array NUM_ROBOTS*3
Init position of each robots
numbers: int
total simulation time
flag: int, observation model
1 range-bearing and transform to relative position
0 range-bearing
-1 relative pose
types: int
-1 Dead Reckon(DR)
0 Decentralized EKF(BDA)
2 Decentralized EKF with CU(DCL-CU)
6 Decentralized EKF(DMV)
12 CI-CU
28 multi-centralized + M-estimation
Seed: int
random seed
'''
print(f'{comm_fail_prob}: Seed={Seed}')
np.random.seed(Seed)
print(
f"meas_bia_prob = {meas_bia_prob}, comm_fail_prob = {comm_fail_prob}")
NUM_ROBOTS = parameters.NUM_ROBOTS
LANDMARK_NUM = parameters.LANDMARK_NUM
n2 = len(types) # the number of algorithms
# Initialize
# GroundTruth of each robot
true_robots = [Robot_true(X=init_X[r], _id=r) for r in range(NUM_ROBOTS)]
algs_robots = {}
if n2:
for type in types:
if type == 0:
algs_robots[0] = [Robot_BDA_EKF(
X=init_X[r], _id=r, NUM_ROBOTS=NUM_ROBOTS, flag=flag, LANDMARK_POS=LANDMARK_POS) for r in range(NUM_ROBOTS)]
elif type == 2:
algs_robots[2] = [Robot_BDA_EKF_CU(
X=init_X[r], _id=r, NUM_ROBOTS=NUM_ROBOTS, flag=flag, LANDMARK_POS=LANDMARK_POS) for r in range(NUM_ROBOTS)]
elif type == 6:
algs_robots[6] = [Robot_DMV(X=init_X[r], _id=r, NUM_ROBOTS=NUM_ROBOTS,
flag=flag, LANDMARK_POS=LANDMARK_POS) for r in range(NUM_ROBOTS)]
elif type == 12:
algs_robots[12] = [Robot_CI_CU(
X=init_X[r], _id=r, NUM_ROBOTS=NUM_ROBOTS, flag=flag, LANDMARK_POS=LANDMARK_POS) for r in range(NUM_ROBOTS)]
elif type == 28:
algs_robots[28] = [Robot_GS_LRHKF(
initial_s=init_X, _id=r, NUM_ROBOTS=NUM_ROBOTS, flag=flag, LANDMARK_POS=LANDMARK_POS) for r in range(NUM_ROBOTS)]
dr_robots = None
if -1 in types:
dr_robots = [Robot(X=init_X[r], _id=r, NUM_ROBOTS=NUM_ROBOTS)
for r in range(NUM_ROBOTS)]
# cen_robots = Robots_CCL(X=init_X[0:NUM_ROBOTS], flag = flag)
if mission in {4, 5}:
list_times = []
list_RMSE = {}
draw.plt.style.use(['science', 'ieee', 'no-latex'])
fig2, ax2 = draw.plt.subplots(1, 1)
for robot_i in true_robots:
robot_i.draw(ax2)
robot_i.plot_shape(ax2)
cou = 0
for type in types:
list_RMSE[type] = []
if not type == -1:
for robot_i in algs_robots[type]:
robot_i.draw(ax2, draw.EACH_COLOR[cou], draw.LABELS[type])
cou += 1
if -1 in types:
list_RMSE[-1] = []
for robot_i in dr_robots:
robot_i.draw(ax2, draw.EACH_COLOR[cou], draw.LABELS[-1])
ax2.legend(loc='upper left')
ax2.axis('equal')
if comm_fail_prob == 0.5:
ax2.text(0.75, 0.9, 'Time: 0s', transform=ax2.transAxes)
ax2.text(0.75, 0.85, r'$\tau={:.{}f}$'.format(
meas_bia_prob, 2), transform=ax2.transAxes)
ax2.text(0.75, 0.05, r'$\rho=$' +
str(comm_fail_prob), transform=ax2.transAxes)
ax2.set_xlabel(r'$x$ [m]')
if comm_fail_prob == 0.1:
ax2.set_ylabel(r'$y$ [m]')
path1 = './video-' + str(mission) + '/Tra-' + str(comm_fail_prob)
if not os.path.exists(path1):
os.makedirs(path1)
draw.plt.savefig(path1 + '/0000.png', dpi=600, bbox_inches='tight')
draw.plt.close()
draw.plt.style.use(['science', 'ieee', 'no-latex'])
fig, ax = draw.plt.subplots(1, 1)
if comm_fail_prob == 0.1:
ax.set_ylabel('ARMSE [m]')
ax.set_xlabel('simulation time [s]')
ax.text(0.45, 0.95, r'$\rho=$' +
str(comm_fail_prob), transform=ax.transAxes)
path2 = './video-' + str(mission) + '/' + str(comm_fail_prob)
if not os.path.exists(path2):
os.makedirs(path2)
draw.plt.savefig(path2 + '/0000.png', dpi=600, bbox_inches='tight')
draw.plt.close()
broadcast_comm_his_GS = {}
for type in types:
if type >= 10:
broadcast_comm_his_GS[type] = np.zeros(
(NUM_ROBOTS, NUM_ROBOTS), dtype=np.uint16)
change_para = [False, False]
if mission in [1, 2, 4]:
change_para = [True, True]
elif mission == 5:
change_para = [False, True]
for count in range(numbers):
# # A part to change some parameters
# if change_para[0]:
# if 4*count > numbers:
# parameters.Q = np.diag(np.tile([.7, .3], NUM_ROBOTS))
# parameters.OMEGA_MAX = 1
# parameters.OMEGA_MIN = -1
# parameters.update_para()
# update_para()
# need_change_type = [False, False]
# for type in types:
# if type >= 20 and not need_change_type[1]:
# from algorithms.DCL_GS import update_para as DCL_GS_up
# DCL_GS_up(parameters.Q, parameters.SIGMA0_OMEGA2, parameters.SIGMA_OMEGA2)
# need_change_type[1] = True
# elif type < 20 and not need_change_type[0]:
# from algorithms.DR import update_para as DR_up
# DR_up(parameters.Q)
# need_change_type[0] = True
# change_para[0] = False
Rv = 0
if mission in [1, 2]:
# v1.1.0 divides 3 parts, change the increase
Rv = 2.7e-3 * (count - 297)
if 3*count >= 2*numbers:
meas_bia_prob = 0
elif 3*count >= numbers:
meas_bia_prob = 0.5
elif mission in [4]: # in [1, 2, 4]
Rv = 1.8e-3*count - 0.8
if 2*count >= numbers:
meas_bia_prob = 0.5
elif mission == 5:
meas_bia_prob = 0.05 * int(count / 91)
Rv = parameters.Rv
else:
Rv = parameters.Rv
############## Motion step starts ########################
# The INPUT velocity and angular velocity follows the normal distribution
_input = [[np.random.randn()*SIGMA_V_INPUT[r] + E_V[r], np.random.randn()
* SIGMA_OMEGA_INPUT[r] + E_OMEGA[r]] for r in range(NUM_ROBOTS)]
# _input = [[0.2, np.random.randn()*SIGMA_OMEGA_INPUT[r] + E_OMEGA[r]] for r in range(NUM_ROBOTS)]
for r in range(NUM_ROBOTS):
if abs(_input[r][0] - E_V[r]) > 3*SIGMA_V_INPUT[r]:
_input[r][0] = E_V[r]
if abs(_input[r][1] - E_OMEGA[r]) > 3*SIGMA_OMEGA_INPUT[r]:
_input[r][1] = E_OMEGA[r]
for type in algs_robots.keys():
for robot_i in algs_robots[type]:
robot_i.reset_rela()
robot_i.motion(v=_input[robot_i._id][0],
omega=_input[robot_i._id][1])
if -1 in types:
for robot_i in dr_robots:
robot_i.motion(v=_input[robot_i._id][0],
omega=_input[robot_i._id][1])
# cen_robots.motion(odometry_input=input)
noises_move = np.random.randn(NUM_ROBOTS, 2) # random for noise
for robot_i in true_robots:
robot_i.update(v=_input[robot_i._id][0], omega=_input[robot_i._id][1],
noise=noises_move[robot_i._id]) # _id must from 0 to NUM_ROBOTS-1
############ observation step starts ############
# Landmark, Absolute observation
# Generate the noise
measure_noises = np.random.randn(NUM_ROBOTS*(LANDMARK_NUM), 3)
measure_bias_happen = np.random.rand(NUM_ROBOTS*(LANDMARK_NUM))
# whether the observation is biased
measure_bias_whether = np.zeros(NUM_ROBOTS*(LANDMARK_NUM), dtype=bool)
for ind in range(NUM_ROBOTS*(LANDMARK_NUM)):
if (measure_bias_happen[ind] < meas_bia_prob):
if (flag == -1):
measure_noises[ind, :] = measure_noises[ind,
:] * BIAS_SIGMA_POSE + MU_POSE * Rv
elif (flag >= 0):
measure_noises[ind, :2] = measure_noises[ind, :2] * \
BIAS_SIGMA_RANGE_BEARING + MU_RANGE_BEARING * Rv
measure_bias_whether[ind] = True
else:
if (flag == -1):
measure_noises[ind, :] = measure_noises[ind, :] * \
np.array([R_1[0, 0], R_1[1, 1], R_1[2, 2]])
elif (flag >= 0):
measure_noises[ind, :2] = measure_noises[ind,
:2] * np.array([R_0[0, 0], R_0[1, 1]])
for type in algs_robots.keys():
for robot_i in algs_robots[type]:
robot_i.measurement_abso(cla_trues=true_robots,
measure_noises=measure_noises[robot_i._id*(
LANDMARK_NUM):robot_i._id*(LANDMARK_NUM)+(LANDMARK_NUM), :],
measure_bias_whether=measure_bias_whether[robot_i._id*(LANDMARK_NUM):robot_i._id*(LANDMARK_NUM)+(LANDMARK_NUM)])
robot_i.abso_meas_correct(count)
robot_i.reset_abso()
measure_noises = np.random.randn(NUM_ROBOTS*(NUM_ROBOTS), 3)
measure_bias_happen = np.random.rand(NUM_ROBOTS*(NUM_ROBOTS))
# whether the observation is biased
measure_bias_whether = np.zeros(NUM_ROBOTS*(NUM_ROBOTS), dtype=bool)
for ind in range(NUM_ROBOTS*(NUM_ROBOTS)):
if (measure_bias_happen[ind] < meas_bia_prob):
if (flag == -1):
measure_noises[ind, :] = measure_noises[ind,
:] * BIAS_SIGMA_POSE + MU_POSE * Rv
elif (flag >= 0):
measure_noises[ind, :2] = measure_noises[ind, :2] * \
BIAS_SIGMA_RANGE_BEARING + MU_RANGE_BEARING * Rv
measure_bias_whether[ind] = True
else:
if (flag == -1):
measure_noises[ind, :] = measure_noises[ind, :] * \
np.array([R_1[0, 0], R_1[1, 1], R_1[2, 2]])
elif (flag >= 0):
measure_noises[ind, :2] = measure_noises[ind,
:2] * np.array([R_0[0, 0], R_0[1, 1]])
# Relative observaion
# Only Resilient systems: when measure, precise later whether commuting or not
for type in algs_robots.keys():
if type < 20:
continue
for robot_i in algs_robots[type]:
robot_i.measurement_rela(cla_trues=true_robots,
measure_noises=measure_noises[robot_i._id*(
NUM_ROBOTS):robot_i._id*(NUM_ROBOTS)+(NUM_ROBOTS), :],
measure_bias_whether=measure_bias_whether[robot_i._id*(NUM_ROBOTS):robot_i._id*(NUM_ROBOTS)+(NUM_ROBOTS)])
robot_i.rela_meas_correct(count)
# Observation and communication which couples
# Observation every 10 times
# n2>0 -> have decentralized
if ((not (count+1) % COMM_RATE) and n2):
for type in algs_robots.keys():
if type >= 20:
continue
for robot_i in algs_robots[type]:
robot_i.measurement_rela(cla_trues=true_robots,
measure_noises=measure_noises[robot_i._id*(
NUM_ROBOTS):robot_i._id*(NUM_ROBOTS)+(NUM_ROBOTS), :],
measure_bias_whether=measure_bias_whether[robot_i._id*(NUM_ROBOTS):robot_i._id*(NUM_ROBOTS)+(NUM_ROBOTS)])
# To keep the noise in the same values
communicate_noises = np.random.rand(NUM_ROBOTS**2, 60)
# Centralized
# for __ in range(NUM_ROBOTS):
# cen_robots.reset()
# cen_robots.observation(_id = __, cla_trues=true_robots, measure_noises = measure_noises[__*NUM_ROBOTS:__*NUM_ROBOTS+NUM_ROBOTS,:])
# if communicate_noises[__*NUM_ROBOTS+__, 0] > comm_fail_prob:
# cen_robots.rela_meas_ekf(_id = __)
############ communication step starts ############
# This's communicate time needed, assumed that they measured at the same time
# NUM_ROBOTS**2 considers more communications
# Neglect the computation times nd the check the broadcasts times and the times sending message to the broadcasts
communicate_time = np.random.rand(NUM_ROBOTS*60)*DELTA_T*2
# circle all algorithms
for type in algs_robots.keys():
# No_more_robots = set()
communicate_time_copy = communicate_time.copy()
robot_comm_order = sorted(
np.arange(NUM_ROBOTS), key=lambda i: communicate_time_copy[:NUM_ROBOTS][i])
# only for picking which index in communicate_time_copy
comm_times = np.zeros(NUM_ROBOTS, dtype=np.uint8)
# Times about send ing the message
send_times = np.zeros((NUM_ROBOTS, NUM_ROBOTS), dtype=int)
if type in {0, 1, 3, 4}: # DCL request, only EKF at one robot
# index is asked for belief before filtering by value
asked4belief_before = np.ones(NUM_ROBOTS, dtype=int)*-1
# received_after = np.zeros(NUM_ROBOTS, dtype=bool) # Received after filtering, and work, so no repeated
broadcast_matched = np.zeros(
NUM_ROBOTS, dtype=bool) # For match
send_back_index = np.ones(
NUM_ROBOTS, dtype=int)*-1 # Index
# X_current = np.zeros((NUM_ROBOTS, 3)) # Current pose
# P_current = np.zeros((NUM_ROBOTS, 3, 3)) # Current covariance
# for robot_i in algs_robots[type]:
# X_current[robot_i._id] = robot_i.X.copy()
# P_current[robot_i._id] = robot_i.P.copy()
'''
If robot i measures robot j, 3 steps happen:
1. robot i asks robot j for robot j's belief
2. robot j sends its belief to robot i
3. robot i sends robot j's belief after filter to robot j
Q: Why replacing the origin communicate_time_copy with new feedback time?
Answer: Because the origin sending-message time is later than now,
and the communication must happen one2one.
'''
while communicate_time_copy[robot_comm_order[0]] < DELTA_T*COMM_RATE:
robot_i = algs_robots[type][robot_comm_order[0]]
if broadcast_matched[robot_i._id]:
# Send my own belief and ask your belief(if needed) (2rd)
if asked4belief_before[robot_i._id] >= 0:
r = asked4belief_before[robot_i._id]
if communicate_noises[robot_i._id*NUM_ROBOTS + r, send_times[robot_i._id, r]] > comm_fail_prob:
asked4belief_before[robot_i._id] = -1
can = algs_robots[type][r].communicate2_1(
robot_j=robot_i, get_other=False)
if (robot_i._id in algs_robots[type][r].sort_pair_request):
algs_robots[type][r].sort_pair_request.remove(
robot_i._id)
if can:
algs_robots[type][r].pair_history[robot_i._id] = count
communicate_time_copy[r] = communicate_time_copy[robot_comm_order[0]]
comm_times[r] += 1
communicate_time_copy[r] += communicate_time_copy[NUM_ROBOTS *
comm_times[r] + r]
if robot_i.measuring[r] or can:
# NEED...
# Next, r sends its belief to robot_i, whatever
send_back_index[r] = robot_i._id
communicate_time_copy[robot_comm_order[0]] += 1
else:
broadcast_matched[r], broadcast_matched[robot_i._id] = False, False
comm_times[robot_comm_order[0]] += 1
communicate_time_copy[robot_comm_order[0]] += communicate_time_copy[NUM_ROBOTS *
comm_times[robot_comm_order[0]] + robot_comm_order[0]]
else:
communicate_time_copy[robot_comm_order[0]] -= (
communicate_time_copy[NUM_ROBOTS*comm_times[robot_comm_order[0]]+robot_comm_order[0]] - DELTA_T*2)
comm_times[robot_comm_order[0]] += 1
communicate_time_copy[robot_comm_order[0]] += communicate_time_copy[NUM_ROBOTS *
comm_times[robot_comm_order[0]] + robot_comm_order[0]]
send_times[robot_i._id, r] += 1
# robot_comm_order = sorted(np.arange(NUM_ROBOTS), key = lambda i: communicate_time_copy[:NUM_ROBOTS][i])
# r is major, robot_i, who? just sender(3rd)
elif send_back_index[robot_i._id] >= 0:
r = send_back_index[robot_i._id]
# Receive the belief(fused or not), r maybe fuse or just replace(3rd)
if communicate_noises[robot_i._id*NUM_ROBOTS+r, send_times[robot_i._id, r]] > comm_fail_prob:
# r receives robot_i's belief(3nd)
can = 0
if algs_robots[type][r].measuring[robot_i._id]:
can = algs_robots[type][r].communicate2_1(
robot_j=robot_i, get_other=False)
if (robot_i._id in algs_robots[type][r].sort_pair_request):
algs_robots[type][r].sort_pair_request.remove(
robot_i._id)
# NEED: ranges from 0 to NUM_ROBOTS-1
communicate_time_copy[r] = communicate_time_copy[robot_comm_order[0]]
send_back_index[robot_i._id] = -1
# If succeed
if can:
algs_robots[type][r].pair_history[robot_i._id] = count
# If just robot_i succeed
# else:
else:
algs_robots[type][r].communicate2_2(
robot_i=robot_i)
# For the next match
broadcast_matched[r], broadcast_matched[robot_i._id] = False, False
algs_robots[type][r].X_prediction = algs_robots[type][r].X.copy(
)
algs_robots[type][r].P_prediction = algs_robots[type][r].P.copy(
)
robot_i.X_prediction, robot_i.P_prediction = robot_i.X.copy(), robot_i.P.copy()
comm_times[r] += 1
communicate_time_copy[r] += communicate_time_copy[NUM_ROBOTS *
comm_times[r] + r]
send_back_index[robot_i._id] = -1
# Fail to send, and resend at the next time
else:
communicate_time_copy[robot_comm_order[0]] -= (
communicate_time_copy[NUM_ROBOTS*comm_times[robot_comm_order[0]] + robot_comm_order[0]] - DELTA_T*2)
comm_times[robot_comm_order[0]] += 1
communicate_time_copy[robot_comm_order[0]] += communicate_time_copy[NUM_ROBOTS *
comm_times[robot_comm_order[0]] + robot_comm_order[0]]
send_times[robot_i._id, r] += 1
robot_comm_order = sorted(
np.arange(NUM_ROBOTS), key=lambda i: communicate_time_copy[:NUM_ROBOTS][i])
continue
pick = 0
while (len(robot_i.sort_pair_request)):
r = robot_i.sort_pair_request[pick]
if broadcast_matched[r]:
pick += 1
if pick == len(robot_i.sort_pair_request):
pick = -1
# robot_i.sort_pair_request = []
communicate_time_copy[robot_comm_order[0]] -= (
communicate_time_copy[NUM_ROBOTS*comm_times[robot_comm_order[0]]+robot_comm_order[0]] - DELTA_T*2)
comm_times[robot_comm_order[0]] += 1
communicate_time_copy[robot_comm_order[0]] += communicate_time_copy[NUM_ROBOTS *
comm_times[robot_comm_order[0]] + robot_comm_order[0]]
robot_comm_order = sorted(
np.arange(NUM_ROBOTS), key=lambda i: communicate_time_copy[:NUM_ROBOTS][i])
# No_more_robots.add(robot_i._id)
break
else:
break
if pick >= 0 and len(robot_i.sort_pair_request):
# Request (1st)
if robot_i.measuring[r] and communicate_noises[robot_i._id*NUM_ROBOTS+r, send_times[robot_i._id, r]] > comm_fail_prob:
asked4belief_before[r] = robot_i._id
# For match
broadcast_matched[r], broadcast_matched[robot_i._id] = True, True
communicate_time_copy[r] = communicate_time_copy[robot_comm_order[0]]
# waiting ...
communicate_time_copy[robot_comm_order[0]] += 1
# NEED: Robot's index ranges from 0 to NUM_ROBOTS-1
comm_times[r] += 1
# communicate_time_copy add communicate times: r->robot_i
communicate_time_copy[r] += communicate_time_copy[NUM_ROBOTS *
comm_times[r] + r]
# Cannot send the message to that robot, waiting time and send at the next time
else:
communicate_time_copy[robot_comm_order[0]] -= (
communicate_time_copy[NUM_ROBOTS*comm_times[robot_comm_order[0]]+robot_comm_order[0]] - DELTA_T*2)
comm_times[robot_comm_order[0]] += 1
communicate_time_copy[robot_comm_order[0]] += communicate_time_copy[NUM_ROBOTS *
comm_times[robot_comm_order[0]] + robot_comm_order[0]]
send_times[robot_i._id, r] += 1
# Not any other robot needed to EKF
else:
communicate_time_copy[robot_comm_order[0]] += 2
robot_comm_order = sorted(
np.arange(NUM_ROBOTS), key=lambda i: communicate_time_copy[:NUM_ROBOTS][i])
# (DCL no request, origin) now: BDA-CU with request, match
elif type in {2, 5}:
# index is asked for belief before filtering by value
asked4belief_before = np.ones(NUM_ROBOTS, dtype=int)*-1
broadcast_matched = np.zeros(
NUM_ROBOTS, dtype=bool) # For match
broadcast_filtered = np.zeros(
(NUM_ROBOTS, NUM_ROBOTS), dtype=bool) # For match
# Value>=0 -> index-CONNECTION-build_connection[index]
build_connection = np.ones(NUM_ROBOTS, dtype=int)*-1
# Value>=0 means index should send info back to Value
send_back_index = np.ones(
NUM_ROBOTS, dtype=int)*-1 # Index
# Value>=0: Just for CU
send4CU = np.ones(NUM_ROBOTS, dtype=int)*-1 # Index
while communicate_time_copy[robot_comm_order[0]] < DELTA_T*COMM_RATE:
robot_i = algs_robots[type][robot_comm_order[0]]
if broadcast_matched[robot_i._id]:
# Send my own belief and ask your belief(if needed) (2rd)
if asked4belief_before[robot_i._id] >= 0:
r = asked4belief_before[robot_i._id]
if communicate_noises[robot_i._id*NUM_ROBOTS + r, send_times[robot_i._id, r]] > comm_fail_prob:
asked4belief_before[robot_i._id] = -1
can = algs_robots[type][r].communicate2_1(
robot_j=robot_i, get_other=False)
if (robot_i._id in algs_robots[type][r].sort_pair_request):
algs_robots[type][r].sort_pair_request.remove(
robot_i._id)
if can:
broadcast_filtered[r,
robot_i._id] = True
algs_robots[type][r].pair_history[robot_i._id] = count
communicate_time_copy[r] = communicate_time_copy[robot_comm_order[0]]
comm_times[r] += 1
communicate_time_copy[r] += communicate_time_copy[NUM_ROBOTS *
comm_times[r] + r]
if robot_i.measuring[r] or can:
# NEED...
# Next, r sends its belief to robot_i, whatever
send_back_index[r] = robot_i._id
communicate_time_copy[robot_comm_order[0]] += 1
else:
broadcast_matched[r], broadcast_matched[robot_i._id] = False, False
comm_times[robot_comm_order[0]] += 1
communicate_time_copy[robot_comm_order[0]] += communicate_time_copy[NUM_ROBOTS *
comm_times[robot_comm_order[0]] + robot_comm_order[0]]
else:
communicate_time_copy[robot_comm_order[0]] -= (
communicate_time_copy[NUM_ROBOTS*comm_times[robot_comm_order[0]]+robot_comm_order[0]] - DELTA_T*2)
comm_times[robot_comm_order[0]] += 1
communicate_time_copy[robot_comm_order[0]] += communicate_time_copy[NUM_ROBOTS *
comm_times[robot_comm_order[0]] + robot_comm_order[0]]
send_times[robot_i._id, r] += 1
# robot_comm_order = sorted(np.arange(NUM_ROBOTS), key = lambda i: communicate_time_copy[:NUM_ROBOTS][i])
# r is major, robot_i, who? just sender(3rd)
elif send_back_index[robot_i._id] >= 0:
r = send_back_index[robot_i._id]
# Receive the belief(fused or not), r maybe fuse or just replace(3rd)
if communicate_noises[robot_i._id*NUM_ROBOTS+r, send_times[robot_i._id, r]] > comm_fail_prob:
# r receives robot_i's belief(3nd)
can = 0
if algs_robots[type][r].measuring[robot_i._id]:
can = algs_robots[type][r].communicate2_1(
robot_j=robot_i, get_other=False)
if (robot_i._id in algs_robots[type][r].sort_pair_request):
algs_robots[type][r].sort_pair_request.remove(
robot_i._id)
# NEED: ranges from 0 to NUM_ROBOTS-1
communicate_time_copy[r] = communicate_time_copy[robot_comm_order[0]]
send_back_index[robot_i._id] = -1
# If succeed
if can:
broadcast_filtered[r,
robot_i._id] = True
algs_robots[type][r].pair_history[robot_i._id] = count
# If robot_i also succeed
if broadcast_filtered[robot_i._id, r]:
algs_robots[type][r].communicate2_2CU(
robot_i=robot_i)
# NEED: ranges from 0 to NUM_ROBOTS-1
# communicate_time_copy[r] = communicate_time_copy[robot_comm_order[0]]
send4CU[r] = robot_i._id
communicate_time_copy[robot_comm_order[0]] += 1
# send_back_index[r] = robot_i._id
# If just robot_i succeed
# else:
elif broadcast_filtered[robot_i._id, r]:
algs_robots[type][r].communicate2_2(
robot_i=robot_i)
# All fails
# For the next match
broadcast_matched[r], broadcast_matched[robot_i._id] = False, False
algs_robots[type][r].X_prediction = algs_robots[type][r].X.copy(
)
algs_robots[type][r].P_prediction = algs_robots[type][r].P.copy(
)
robot_i.X_prediction, robot_i.P_prediction = robot_i.X.copy(), robot_i.P.copy()
comm_times[robot_comm_order[0]] += 1
communicate_time_copy[robot_comm_order[0]] += communicate_time_copy[NUM_ROBOTS *
comm_times[robot_comm_order[0]] + robot_comm_order[0]]
comm_times[r] += 1
communicate_time_copy[r] += communicate_time_copy[NUM_ROBOTS *
comm_times[r] + r]
send_back_index[robot_i._id] = -1
# Fail to send, and resend at the next time
else:
communicate_time_copy[robot_comm_order[0]] -= (
communicate_time_copy[NUM_ROBOTS*comm_times[robot_comm_order[0]] + robot_comm_order[0]] - DELTA_T*2)
comm_times[robot_comm_order[0]] += 1
communicate_time_copy[robot_comm_order[0]] += communicate_time_copy[NUM_ROBOTS *
comm_times[robot_comm_order[0]] + robot_comm_order[0]]
send_times[robot_i._id, r] += 1
# Receive the fused estimate or not(3rd). r needs to CU or =
# this means that r has received robot_i's info before because 2nd(4th)
elif send4CU[robot_i._id] >= 0:
r = send4CU[robot_i._id]
if communicate_noises[robot_i._id*NUM_ROBOTS+r, send_times[robot_i._id, r]] > comm_fail_prob:
# both can EKF, CU
if broadcast_filtered[r, robot_i._id] and broadcast_filtered[robot_i._id, r]:
algs_robots[type][r].communicate2_2CU(
robot_i=robot_i)
# For the next match
broadcast_matched[r], broadcast_matched[robot_i._id] = False, False
algs_robots[type][r].X_prediction = algs_robots[type][r].X.copy(
)
algs_robots[type][r].P_prediction = algs_robots[type][r].P.copy(
)
robot_i.X_prediction, robot_i.P_prediction = robot_i.X.copy(), robot_i.P.copy()
send4CU[robot_i._id] = -1
# NEED: ranges from 0 to NUM_ROBOTS-1
communicate_time_copy[r] = communicate_time_copy[robot_comm_order[0]]
comm_times[r] += 1
communicate_time_copy[r] += communicate_time_copy[NUM_ROBOTS *
comm_times[r] + r]
# Fail to send and resend at the next time
else:
communicate_time_copy[robot_comm_order[0]] -= (
communicate_time_copy[NUM_ROBOTS*comm_times[robot_comm_order[0]] + robot_comm_order[0]] - DELTA_T*2)
comm_times[robot_comm_order[0]] += 1
communicate_time_copy[robot_comm_order[0]] += communicate_time_copy[NUM_ROBOTS *
comm_times[robot_comm_order[0]] + robot_comm_order[0]]
send_times[robot_i._id, r] += 1
robot_comm_order = sorted(
np.arange(NUM_ROBOTS), key=lambda i: communicate_time_copy[:NUM_ROBOTS][i])
continue
pick = 0
while (len(robot_i.sort_pair_request)):
r = robot_i.sort_pair_request[pick]
if broadcast_matched[r]:
pick += 1
if pick == len(robot_i.sort_pair_request):
pick = -1
communicate_time_copy[robot_comm_order[0]] -= (
communicate_time_copy[NUM_ROBOTS*comm_times[robot_comm_order[0]]+robot_comm_order[0]] - DELTA_T*2)
comm_times[robot_comm_order[0]] += 1
communicate_time_copy[robot_comm_order[0]] += communicate_time_copy[NUM_ROBOTS *
comm_times[robot_comm_order[0]] + robot_comm_order[0]]
robot_comm_order = sorted(
np.arange(NUM_ROBOTS), key=lambda i: communicate_time_copy[:NUM_ROBOTS][i])
break
else:
break
if pick >= 0 and len(robot_i.sort_pair_request):
# Request (1st)
if robot_i.measuring[r] and communicate_noises[robot_i._id*NUM_ROBOTS+r, send_times[robot_i._id, r]] > comm_fail_prob:
asked4belief_before[r] = robot_i._id
# For match
broadcast_matched[r], broadcast_matched[robot_i._id] = True, True
communicate_time_copy[r] = communicate_time_copy[robot_comm_order[0]]
# waiting ...
communicate_time_copy[robot_comm_order[0]] += 1
# NEED: Robot's index ranges from 0 to NUM_ROBOTS-1
comm_times[r] += 1
# communicate_time_copy add communicate times: r->robot_i
communicate_time_copy[r] += communicate_time_copy[NUM_ROBOTS *
comm_times[r] + r]
# Cannot send the message to that robot, waiting time and send at the next time
else:
communicate_time_copy[robot_comm_order[0]] -= (
communicate_time_copy[NUM_ROBOTS*comm_times[robot_comm_order[0]]+robot_comm_order[0]] - DELTA_T*2)
comm_times[robot_comm_order[0]] += 1
communicate_time_copy[robot_comm_order[0]] += communicate_time_copy[NUM_ROBOTS *
comm_times[robot_comm_order[0]] + robot_comm_order[0]]
send_times[robot_i._id, r] += 1
# Not any other robot needed to EKF
else:
communicate_time_copy[robot_comm_order[0]] += 2
robot_comm_order = sorted(
np.arange(NUM_ROBOTS), key=lambda i: communicate_time_copy[:NUM_ROBOTS][i])
# print('a')
# Zhu_Kia from paper, both robot EKF both measurement(If it is, not others needed)
elif type in {6, 7}:
broadcast_matched = np.zeros(
NUM_ROBOTS, dtype=bool) # For match
# send_back_index[i] = j, means robot_i sends its belief to robot_j
send_back_index = np.ones(NUM_ROBOTS, dtype=int)*-1
# Still can communicate
while communicate_time_copy[robot_comm_order[0]] < DELTA_T*COMM_RATE:
# which robot's sending message could be received first
robot_i = algs_robots[type][robot_comm_order[0]]
if send_back_index[robot_i._id] >= 0:
# robot_i has received the belief and measurement before,
# then this time should send back(2nd)
# r = That robot's _id. The one robot_i sends to
r = send_back_index[robot_i._id]
if communicate_noises[robot_i._id*NUM_ROBOTS+r, send_times[robot_i._id, r]] > comm_fail_prob:
# Successfully send back
can_2 = algs_robots[type][r].communicate1_1(
robot_j=robot_i)
if can_2:
algs_robots[type][r].pair_history[robot_i._id] = count
algs_robots[type][r].sort_pair_request.remove(
robot_i._id)
# Anyway, cancel it, meaning having received it, both robots can update prediction
broadcast_matched[r], broadcast_matched[robot_i._id] = False, False
send_back_index[robot_i._id] = -1
# Update both prediction believes
robot_i.X_prediction = robot_i.X.copy()
robot_i.P_prediction = robot_i.P.copy()
algs_robots[type][r].X_prediction = algs_robots[type][r].X.copy(
)
algs_robots[type][r].P_prediction = algs_robots[type][r].P.copy(
)
# That robot no need to wait, continue to send
communicate_time_copy[r] = communicate_time_copy[robot_comm_order[0]]
comm_times[r] += 1
communicate_time_copy[r] += communicate_time_copy[NUM_ROBOTS *
comm_times[r] + r]
else:
# waiting the change of broadcast_matched
communicate_time_copy[robot_comm_order[0]] -= (
communicate_time_copy[NUM_ROBOTS*comm_times[robot_comm_order[0]] + robot_comm_order[0]] - DELTA_T*2)
# Anyway, robot_i's communication arrival times need to update
comm_times[robot_comm_order[0]] += 1
communicate_time_copy[robot_comm_order[0]] += communicate_time_copy[NUM_ROBOTS *
comm_times[robot_comm_order[0]] + robot_comm_order[0]]
send_times[robot_i._id, r] += 1
# for r in robot_i.sort_pair_request:
elif (len(robot_i.sort_pair_request)):
# robot_i measures other robots and looks for the 1st one to communicate
r = robot_i.sort_pair_request[0]
can_2 = 0
# Request, and send own pose(1st)
if robot_i.measuring[r] and communicate_noises[robot_i._id*NUM_ROBOTS+r, send_times[robot_i._id, r]] > comm_fail_prob:
# Anyway, broadcast them
# For match
broadcast_matched[r], broadcast_matched[robot_i._id] = True, True
can_2 = algs_robots[type][r].communicate1_1(
robot_j=robot_i)
# Can filter
if can_2:
algs_robots[type][r].pair_history[robot_i._id] = count
# r should send back its prediction belief
send_back_index[r] = robot_i._id
if robot_i._id not in algs_robots[type][r].sort_pair_request or \
(robot_i._id in algs_robots[type][r].sort_pair_request and
robot_i._id != algs_robots[type][r].sort_pair_request[0]):
# Originally r does not send to robot_i
# Now, feedback first, give up the original sending
comm_times[r] += 1
communicate_time_copy[r] = communicate_time_copy[robot_comm_order[0]
] + communicate_time_copy[NUM_ROBOTS*comm_times[r] + r]
if robot_i._id in algs_robots[type][r].sort_pair_request:
algs_robots[type][r].sort_pair_request.remove(
robot_i._id)
# robot_i: waiting
communicate_time_copy[robot_comm_order[0]] += 1
# Cannot send the message to that robot, still waiting for broadcast_matched
else:
# robot_i resends
communicate_time_copy[robot_comm_order[0]] -= (
communicate_time_copy[NUM_ROBOTS*comm_times[robot_comm_order[0]]+robot_comm_order[0]] - DELTA_T*2)
comm_times[robot_comm_order[0]] += 1
comm_times[robot_comm_order[0]] += communicate_time_copy[NUM_ROBOTS *
comm_times[robot_comm_order[0]] + robot_comm_order[0]]
send_times[robot_i._id, r] += 1
# robot_i has no one to communicate
else:
communicate_time_copy[robot_comm_order[0]] += 2
robot_comm_order = sorted(
np.arange(NUM_ROBOTS), key=lambda i: communicate_time_copy[:NUM_ROBOTS][i])
# only communicate once: CI-CU
elif type >= 10 and type < 20:
for r in range(NUM_ROBOTS):
broadcast_comm_his_GS[type][r][r] = count
for _id in range(NUM_ROBOTS):
if _id != r and not algs_robots[type][r].measuring[_id]:
broadcast_comm_his_GS[type][r][_id] += numbers
while communicate_time_copy[robot_comm_order[0]] < DELTA_T*COMM_RATE:
robot_i = algs_robots[type][robot_comm_order[0]]
comm_1st_order = sorted(np.arange(
NUM_ROBOTS), key=lambda i: broadcast_comm_his_GS[type][robot_i._id][i])
if (broadcast_comm_his_GS[type][robot_i._id][comm_1st_order[0]] == count):
# No more communication
communicate_time_copy[robot_comm_order[0]] += 1
else:
r = comm_1st_order[0]
# robot_i -> r, no matter success or fail
if communicate_noises[robot_i._id*NUM_ROBOTS+r, send_times[robot_i._id, r]] > comm_fail_prob:
can = algs_robots[type][r].communicate1(
robot_i)
# Received and update with others
if can:
broadcast_comm_his_GS[type][robot_i._id][r] = count
# Received but cannot update, temporally no use
else:
broadcast_comm_his_GS[type][robot_i._id][r] += numbers
else:
communicate_time_copy[robot_comm_order[0]] -= (
communicate_time_copy[NUM_ROBOTS*comm_times[robot_comm_order[0]] + robot_comm_order[0]] - DELTA_T*2)
# Whether being received, the robot_i's X is updated and sent at the next time
robot_i.X_prediction = robot_i.X.copy()
robot_i.P_prediction = robot_i.P.copy()
send_times[robot_i._id, r] += 1
comm_times[robot_comm_order[0]] += 1
communicate_time_copy[robot_comm_order[0]] += communicate_time_copy[NUM_ROBOTS *
comm_times[robot_comm_order[0]] + robot_comm_order[0]]
robot_comm_order = sorted(
np.arange(NUM_ROBOTS), key=lambda i: communicate_time_copy[:NUM_ROBOTS][i])
broadcast_comm_his_GS[type] = [
[val - numbers if val >= numbers else val for val in row] for row in broadcast_comm_his_GS[type]]
# resilient
elif type >= 20:
for r in range(NUM_ROBOTS):
broadcast_comm_his_GS[type][r][r] = count
while communicate_time_copy[robot_comm_order[0]] < DELTA_T*COMM_RATE:
robot_i = algs_robots[type][robot_comm_order[0]]
# sort the broadcast_comm_his_GS[type][robot_i._id]