#
# ISC License
#
# Copyright (c) 2016, Autonomous Vehicle Systems Lab, University of Colorado at Boulder
#
# Permission to use, copy, modify, and/or distribute this software for any
# purpose with or without fee is hereby granted, provided that the above
# copyright notice and this permission notice appear in all copies.
#
# THE SOFTWARE IS PROVIDED "AS IS" AND THE AUTHOR DISCLAIMS ALL WARRANTIES
# WITH REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF
# MERCHANTABILITY AND FITNESS. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR
# ANY SPECIAL, DIRECT, INDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES
# WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN
# ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT OF
# OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE.
#
r"""
Overview
--------
This script is called by OpNavScenarios/OpNavMC/MonteCarlo.py in order to make MC data.
"""
# Import utilities
from Basilisk.utilities import orbitalMotion, macros, unitTestSupport
from Basilisk.utilities import RigidBodyKinematics as rbk
# Get current file path
import sys, os, inspect, time, signal, subprocess
filename = inspect.getframeinfo(inspect.currentframe()).filename
path = os.path.dirname(os.path.abspath(filename))
from sys import platform
# Import master classes: simulation base class and scenario base class
sys.path.append(path + '/../..')
from BSK_OpNav import BSKSim, BSKScenario
import BSK_OpNavDynamics, BSK_OpNavFsw
import numpy as np
# Import plotting file for your scenario
sys.path.append(path + '/../../plottingOpNav')
import OpNav_Plotting as BSK_plt
# Create your own scenario child class
[docs]class scenario_OpNav(BSKSim):
"""Main Simulation Class"""
def __init__(self):
super(scenario_OpNav, self).__init__(BSKSim)
self.fswRate = 0.5
self.dynRate = 0.5
self.set_DynModel(BSK_OpNavDynamics)
self.set_FswModel(BSK_OpNavFsw)
self.initInterfaces()
self.name = 'scenario_opnav'
self.configure_initial_conditions()
def configure_initial_conditions(self):
print('%s: configure_initial_conditions' % self.name)
# Configure Dynamics initial conditions
oe = orbitalMotion.ClassicElements()
oe.a = 18000 * 1E3 # meters
oe.e = 0.6
oe.i = 10 * macros.D2R
oe.Omega = 25. * macros.D2R
oe.omega = 190. * macros.D2R
oe.f = 80. * macros.D2R # 90 good
mu = self.get_DynModel().marsGravBody.mu
rN, vN = orbitalMotion.elem2rv(mu, oe)
orbitalMotion.rv2elem(mu, rN, vN)
bias = [0, 0, -2]
rError= np.array([10000.,10000., -10000])
vError= np.array([100, -10, 10])
MRP= [0,-0.3,0]
self.get_FswModel().relativeODData.stateInit = (rN+rError).tolist() + (vN+vError).tolist()
self.get_DynModel().scObject.hub.r_CN_NInit = unitTestSupport.np2EigenVectorXd(rN) # m - r_CN_N
self.get_DynModel().scObject.hub.v_CN_NInit = unitTestSupport.np2EigenVectorXd(vN) # m/s - v_CN_N
self.get_DynModel().scObject.hub.sigma_BNInit = [[MRP[0]], [MRP[1]], [MRP[2]]] # sigma_BN_B
self.get_DynModel().scObject.hub.omega_BN_BInit = [[0.0], [0.0], [0.0]] # rad/s - omega_BN_B
qNoiseIn = np.identity(6)
qNoiseIn[0:3, 0:3] = qNoiseIn[0:3, 0:3] * 1E-3 * 1E-3
qNoiseIn[3:6, 3:6] = qNoiseIn[3:6, 3:6] * 1E-4 * 1E-4
self.get_FswModel().relativeODData.qNoise = qNoiseIn.reshape(36).tolist()
self.get_FswModel().horizonNavData.noiseSF = 20
def log_outputs(self):
print('%s: log_outputs' % self.name)
# Dynamics process outputs: log messages below if desired.
# FSW process outputs
samplingTime = self.get_FswModel().processTasksTimeStep
# self.TotalSim.logThisMessage(self.get_FswModel().trackingErrorCamData.outputDataName, samplingTime)
# self.TotalSim.logThisMessage(self.get_FswModel().trackingErrorData.outputDataName, samplingTime)
self.TotalSim.logThisMessage(self.get_FswModel().relativeODData.filtDataOutMsgName, samplingTime)
self.TotalSim.logThisMessage(self.get_DynModel().scObject.scStateOutMsgName,samplingTime)
self.TotalSim.logThisMessage(self.get_FswModel().horizonNavData.opNavOutMsgName, samplingTime)
self.TotalSim.logThisMessage(self.get_FswModel().limbFinding.opnavLimbOutMsgName, samplingTime)
return
def pull_outputs(self, showPlots):
print('%s: pull_outputs' % self.name)
# Dynamics process outputs: pull log messages below if any
## Spacecraft true states
position_N = self.pullMessageLogData(
self.get_DynModel().scObject.scStateOutMsgName + ".r_BN_N", range(3))
velocity_N = self.pullMessageLogData(
self.get_DynModel().scObject.scStateOutMsgName + ".v_BN_N", range(3))
## Attitude
sigma_BN = self.pullMessageLogData(
self.get_DynModel().scObject.scStateOutMsgName + ".sigma_BN", range(3))
## Image processing
limb = self.pullMessageLogData(
self.get_FswModel().limbFinding.opnavLimbOutMsgName + ".limbPoints", range(2*2000))
numLimbPoints = self.pullMessageLogData(
self.get_FswModel().limbFinding.opnavLimbOutMsgName + ".numLimbPoints", range(1))
validLimb = self.pullMessageLogData(
self.get_FswModel().limbFinding.opnavLimbOutMsgName + ".valid", range(1))
## OpNav Out
measPos = self.pullMessageLogData(
self.get_FswModel().horizonNavData.opNavOutMsgName + ".r_BN_N", range(3))
r_C = self.pullMessageLogData(
self.get_FswModel().horizonNavData.opNavOutMsgName + ".r_BN_C", range(3))
measCovar = self.pullMessageLogData(
self.get_FswModel().horizonNavData.opNavOutMsgName + ".covar_N", range(3 * 3))
covar_C = self.pullMessageLogData(
self.get_FswModel().horizonNavData.opNavOutMsgName + ".covar_C", range(3 * 3))
NUM_STATES = 6
## Navigation results
navState = self.pullMessageLogData(
self.get_FswModel().relativeODData.filtDataOutMsgName + ".state", range(NUM_STATES))
navCovar = self.pullMessageLogData(
self.get_FswModel().relativeODData.filtDataOutMsgName + ".covar",
range(NUM_STATES * NUM_STATES))
navPostFits = self.pullMessageLogData(
self.get_FswModel().relativeODData.filtDataOutMsgName + ".postFitRes", range(NUM_STATES - 3))
sigma_CB = self.get_DynModel().cameraMRP_CB
sizeMM = self.get_DynModel().cameraSize
sizeOfCam = self.get_DynModel().cameraRez
focal = self.get_DynModel().cameraFocal #in m
pixelSize = []
pixelSize.append(sizeMM[0] / sizeOfCam[0])
pixelSize.append(sizeMM[1] / sizeOfCam[1])
dcm_CB = rbk.MRP2C(sigma_CB)
# Plot results
BSK_plt.clear_all_plots()
stateError = np.zeros([len(position_N[:,0]), NUM_STATES+1])
navCovarLong = np.full([len(position_N[:,0]), 1+NUM_STATES*NUM_STATES], np.nan)
navCovarLong[:,0] = np.copy(position_N[:,0])
stateError[:, 0:4] = np.copy(position_N)
stateError[:,4:7] = np.copy(velocity_N[:,1:])
measError = np.full([len(measPos[:,0]), 4], np.nan)
measError[:,0] = measPos[:,0]
measError_C = np.full([len(measPos[:,0]), 5], np.nan)
measError_C[:,0] = measPos[:,0]
trueRhat_C = np.full([len(numLimbPoints[:,0]), 4], np.nan)
trueR_C = np.full([len(numLimbPoints[:,0]), 4], np.nan)
trueCircles = np.full([len(numLimbPoints[:,0]), 4], np.nan)
trueCircles[:,0] = numLimbPoints[:,0]
trueRhat_C[:,0] = numLimbPoints[:,0]
trueR_C[:,0] = numLimbPoints[:,0]
switchIdx = 0
Rmars = 3396.19*1E3
for j in range(len(stateError[:, 0])):
if stateError[j, 0] in navState[:, 0]:
stateError[j, 1:4] -= navState[j - switchIdx, 1:4]
stateError[j, 4:] -= navState[j - switchIdx, 4:]
else:
stateError[j, 1:] = np.full(NUM_STATES, np.nan)
switchIdx += 1
for i in range(len(numLimbPoints[:,0])):
if numLimbPoints[i,1] > 1E-8:
measError[i, 1:4] = position_N[i +switchIdx, 1:4] - measPos[i, 1:4]
measError_C[i, 4] = np.linalg.norm(position_N[i +switchIdx, 1:4]) - np.linalg.norm(r_C[i, 1:4])
trueR_C[i,1:] = np.dot(np.dot(dcm_CB, rbk.MRP2C(sigma_BN[i +switchIdx, 1:4])) , position_N[i +switchIdx, 1:4])
trueRhat_C[i,1:] = np.dot(np.dot(dcm_CB, rbk.MRP2C(sigma_BN[i +switchIdx, 1:4])) ,position_N[i +switchIdx, 1:4])/np.linalg.norm(position_N[i +switchIdx, 1:4])
trueCircles[i,3] = focal*np.tan(np.arcsin(Rmars/np.linalg.norm(position_N[i,1:4])))/pixelSize[0]
trueRhat_C[i,1:] *= focal/trueRhat_C[i,3]
measError_C[i, 1:4] = trueRhat_C[i,1:] - r_C[i, 1:4]/np.linalg.norm(r_C[i, 1:4])
trueCircles[i, 1] = trueRhat_C[i, 1] / pixelSize[0] + sizeOfCam[0]/2 - 0.5
trueCircles[i, 2] = trueRhat_C[i, 2] / pixelSize[1] + sizeOfCam[1]/2 - 0.5
else:
measCovar[i,1:] = np.full(3*3, np.nan)
covar_C[i, 1:] = np.full(3 * 3, np.nan)
navCovarLong[switchIdx:,:] = np.copy(navCovar)
timeData = position_N[:, 0] * macros.NANO2MIN
BSK_plt.plot_TwoOrbits(position_N, measPos)
BSK_plt.diff_vectors(trueR_C, r_C, validLimb, "Limb")
BSK_plt.plot_limb(limb, numLimbPoints, validLimb, sizeOfCam)
# BSK_plt.AnimatedScatter(sizeOfCam, circleCenters, circleRadii, validCircle)
BSK_plt.plotStateCovarPlot(stateError, navCovarLong)
# BSK_plt.imgProcVsExp(trueCircles, circleCenters, circleRadii, np.array(sizeOfCam))
BSK_plt.plotPostFitResiduals(navPostFits, measCovar)
figureList = {}
if showPlots:
BSK_plt.show_all_plots()
else:
fileName = os.path.basename(os.path.splitext(__file__)[0])
figureNames = ["attitudeErrorNorm", "rwMotorTorque", "rateError", "rwSpeed"]
figureList = BSK_plt.save_all_plots(fileName, figureNames)
return figureList
def run(TheScenario):
TheScenario.log_outputs()
TheScenario.configure_initial_conditions()
TheScenario.get_FswModel().imageProcessing.saveImages = 0
TheScenario.get_DynModel().vizInterface.opNavMode = 1
mode = ["None", "-directComm", "-opNavMode"]
# The following code spawns the Vizard application from python as a function of the mode selected above, and the platform.
if platform != "darwin":
child = subprocess.Popen([TheScenario.vizPath, "--args", mode[TheScenario.get_DynModel().vizInterface.opNavMode],
"tcp://localhost:5556"])
else:
child = subprocess.Popen(
["open", TheScenario.vizPath, "--args", mode[TheScenario.get_DynModel().vizInterface.opNavMode],
"tcp://localhost:5556"])
print("Vizard spawned with PID = " + str(child.pid))
# Configure FSW mode
TheScenario.modeRequest = 'prepOpNav'
# Initialize simulation
TheScenario.InitializeSimulationAndDiscover()
# Configure run time and execute simulation
simulationTime = macros.min2nano(3.)
TheScenario.ConfigureStopTime(simulationTime)
TheScenario.ExecuteSimulation()
TheScenario.modeRequest = 'OpNavAttODLimb'
# TheBSKSim.get_DynModel().SetLocalConfigData(TheBSKSim, 60, True)
simulationTime = macros.min2nano(600.)
TheScenario.ConfigureStopTime(simulationTime)
TheScenario.ExecuteSimulation()
TheScenario.get_DynModel().SpiceObject.unloadSpiceKernel(TheScenario.get_DynModel().SpiceObject.SPICEDataPath, 'de430.bsp')
TheScenario.get_DynModel().SpiceObject.unloadSpiceKernel(TheScenario.get_DynModel().SpiceObject.SPICEDataPath, 'naif0012.tls')
TheScenario.get_DynModel().SpiceObject.unloadSpiceKernel(TheScenario.get_DynModel().SpiceObject.SPICEDataPath,
'de-403-masses.tpc')
TheScenario.get_DynModel().SpiceObject.unloadSpiceKernel(TheScenario.get_DynModel().SpiceObject.SPICEDataPath, 'pck00010.tpc')
return
if __name__ == "__main__":
# Instantiate base simulation
# Configure a scenario in the base simulation
TheScenario = scenario_OpNav()
run(TheScenario)