#
# 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
--------
Using OpenCV, this module loads a pre-trained neural network in order to do Centroid and Apparent Diameter
estimation on an image. More details can be found in Chapter 6 of `Thibaud Teil's PhD thesis <http://hanspeterschaub.info/Papers/grads/ThibaudTeil.pdf>`_.
The script can be run at full length by calling::
python3 scenario_CNNAttOD.py
"""
# Get current file path
import inspect
import os
import sys
import time
from Basilisk.utilities import RigidBodyKinematics as rbk
# Import utilities
from Basilisk.utilities import orbitalMotion, macros, unitTestSupport
filename = inspect.getframeinfo(inspect.currentframe()).filename
path = os.path.dirname(os.path.abspath(filename))
# 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(BSKScenario):
"""Main Simulation Class"""
def __init__(self, masterSim, showPlots=False):
super(scenario_OpNav, self).__init__(masterSim, showPlots)
self.name = 'scenario_opnav'
self.masterSim = masterSim
self.filterUse ="relOD" #"bias" #
# declare additional class variables
self.opNavRec = None
self.circlesRec = None
self.scRec = None
self.filtRec = None
[docs] def log_outputs(self):
# Dynamics process outputs: log messages below if desired.
FswModel = self.masterSim.get_FswModel()
DynModel = self.masterSim.get_DynModel()
# FSW process outputs
samplingTime = self.masterSim.get_FswModel().processTasksTimeStep
if self.filterUse == "relOD":
self.filtRec = FswModel.relativeODData.filtDataOutMsg.recorder(samplingTime)
self.masterSim.AddModelToTask(DynModel.taskName, self.filtRec)
self.opNavRec = FswModel.opnavMsg.recorder(samplingTime)
self.masterSim.AddModelToTask(DynModel.taskName, self.opNavRec)
if self.filterUse == "bias":
self.filtRec = FswModel.pixelLineFilterData.filtDataOutMsg.recorder(samplingTime)
self.masterSim.AddModelToTask(DynModel.taskName, self.filtRec)
self.scRec = DynModel.scObject.scStateOutMsg.recorder(samplingTime)
self.circlesRec = FswModel.opnavCirclesMsg.recorder(samplingTime)
self.masterSim.AddModelToTask(DynModel.taskName, self.scRec)
self.masterSim.AddModelToTask(DynModel.taskName, self.circlesRec)
return
[docs] def pull_outputs(self, showPlots):
# Dynamics process outputs: pull log messages below if any
## Spacecraft true states
position_N = unitTestSupport.addTimeColumn(self.scRec.times(), self.scRec.r_BN_N)
velocity_N = unitTestSupport.addTimeColumn(self.scRec.times(), self.scRec.v_BN_N)
## Attitude
sigma_BN = unitTestSupport.addTimeColumn(self.scRec.times(), self.scRec.sigma_BN)
## Image processing
circleCenters = unitTestSupport.addTimeColumn(self.circlesRec.times(), self.circlesRec.circlesCenters)
circleRadii = unitTestSupport.addTimeColumn(self.circlesRec.times(), self.circlesRec.circlesRadii)
validCircle = unitTestSupport.addTimeColumn(self.circlesRec.times(), self.circlesRec.valid)
if self.filterUse == "bias":
NUM_STATES = 9
## Navigation results
navState = unitTestSupport.addTimeColumn(self.filtRec.times(), self.filtRec.state)
navCovar = unitTestSupport.addTimeColumn(self.filtRec.times(), self.filtRec.covar)
navPostFits = unitTestSupport.addTimeColumn(self.filtRec.times(), self.filtRec.postFitRes)
if self.filterUse == "relOD":
NUM_STATES = 6
## Navigation results
navState = unitTestSupport.addTimeColumn(self.filtRec.times(), self.filtRec.state)
navCovar = unitTestSupport.addTimeColumn(self.filtRec.times(), self.filtRec.covar)
navPostFits = unitTestSupport.addTimeColumn(self.filtRec.times(), self.filtRec.postFitRes)
measPos = unitTestSupport.addTimeColumn(self.opNavRec.times(), self.opNavRec.r_BN_N)
r_C = unitTestSupport.addTimeColumn(self.opNavRec.times(), self.opNavRec.r_BN_C)
measCovar = unitTestSupport.addTimeColumn(self.opNavRec.times(), self.opNavRec.covar_N)
covar_C = unitTestSupport.addTimeColumn(self.opNavRec.times(), self.opNavRec.covar_C)
sigma_CB = self.masterSim.get_DynModel().cameraMRP_CB
sizeMM = self.masterSim.get_DynModel().cameraSize
sizeOfCam = self.masterSim.get_DynModel().cameraRez
focal = self.masterSim.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:])
pixCovar = np.ones([len(circleCenters[:,0]), 3*3+1])
pixCovar[:,0] = circleCenters[:,0]
pixCovar[:,1:]*=np.array([1,0,0,0,1,0,0,0,2])
if self.filterUse == "relOD":
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(circleCenters[:,0]), 4], np.nan)
trueR_C = np.full([len(circleCenters[:,0]), 4], np.nan)
trueCircles = np.full([len(circleCenters[:,0]), 4], np.nan)
trueCircles[:,0] = circleCenters[:,0]
trueRhat_C[:,0] = circleCenters[:,0]
trueR_C[:,0] = circleCenters[:,0]
truth = np.zeros([len(position_N[:,0]), 7])
truth[:,0:4] = np.copy(position_N)
truth[:,4:7] = np.copy(velocity_N[:,1:])
centerBias = np.copy(circleCenters)
radBias = np.copy(circleRadii)
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(circleCenters[:,0])):
if circleCenters[i,1:].any() > 1E-8 or circleCenters[i,1:].any() < -1E-8:
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]
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
if self.filterUse == "bias":
centerBias[i,1:3] = np.round(navState[i, 7:9])
radBias[i,1] = np.round(navState[i, -1])
if self.filterUse == "relOD":
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])
measError_C[i, 1:4] = trueRhat_C[i,1:] - r_C[i, 1:4]/np.linalg.norm(r_C[i, 1:4])
else:
if self.filterUse == "relOD":
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.AnimatedCircles(sizeOfCam, circleCenters, circleRadii, validCircle)
BSK_plt.plot_TwoOrbits(position_N[switchIdx:,:], measPos)
BSK_plt.diff_vectors(trueR_C, r_C, validCircle, "Circ")
BSK_plt.nav_percentages(truth[switchIdx:,:], navState, navCovar, validCircle, "CNN")
BSK_plt.plot_cirlces(circleCenters, circleRadii, validCircle, sizeOfCam)
# # BSK_plt.plot_rate_error(timeData, sigma_BR)
# # BSK_plt.plot_rate_error(timeData, omega_BR_B)
BSK_plt.plotStateCovarPlot(stateError, navCovarLong)
# # BSK_plt.plotStateCovarPlot(measError, measCovar)
# BSK_plt.pixelAndPos(measError_C, position_N[switchIdx:,:], circleCenters, np.array(sizeOfCam))
if self.filterUse == "bias":
circleCenters[i,1:] += centerBias[i,1:]
circleRadii[i,1:] += radBias[i,1:]
BSK_plt.plotPostFitResiduals(navPostFits, pixCovar)
BSK_plt.imgProcVsExp(trueCircles, circleCenters, circleRadii, np.array(sizeOfCam))
# BSK_plt.centerXY(circleCenters, np.array(sizeOfCam))
if self.filterUse == "relOD":
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(showPlots, simTime=None):
# Instantiate base simulation
TheBSKSim = BSKSim(fswRate=0.5, dynRate=0.5)
TheBSKSim.set_DynModel(BSK_OpNavDynamics)
TheBSKSim.set_FswModel(BSK_OpNavFsw)
# Configure a scenario in the base simulation
TheScenario = scenario_OpNav(TheBSKSim, showPlots)
TheScenario.filterUse = "relOD"
TheScenario.log_outputs()
TheScenario.configure_initial_conditions()
TheBSKSim.get_DynModel().cameraMod.saveImages = 0
# opNavMode 1 is used for viewing the spacecraft as it navigates, opNavMode 2 is for headless camera simulation
TheBSKSim.get_DynModel().vizInterface.opNavMode = 2
# The following code spawns the Vizard application from python
mode = ["None", "-directComm", "-noDisplay"]
TheScenario.run_vizard(mode[TheBSKSim.get_DynModel().vizInterface.opNavMode])
# Configure FSW mode
TheScenario.masterSim.modeRequest = 'prepOpNav'
# Initialize simulation
TheBSKSim.InitializeSimulation()
# Configure run time and execute simulation
simulationTime = macros.min2nano(5.)
TheBSKSim.ConfigureStopTime(simulationTime)
print('Starting Execution')
t1 = time.time()
TheBSKSim.ExecuteSimulation()
if TheScenario.filterUse == "bias":
TheScenario.masterSim.modeRequest = 'OpNavAttODB'
if TheScenario.filterUse == "relOD":
TheScenario.masterSim.modeRequest = 'CNNAttOD'
if simTime != None:
simulationTime = macros.min2nano(simTime)
else:
simulationTime = macros.min2nano(600)
TheBSKSim.ConfigureStopTime(simulationTime)
TheBSKSim.ExecuteSimulation()
t2 = time.time()
print('Finished Execution in ', t2-t1, ' seconds. Post-processing results')
#Terminate vizard and show plots
figureList = TheScenario.end_scenario()
return figureList
if __name__ == "__main__":
if not BSK_OpNavFsw.centerRadiusCNNIncluded:
print("centerRadiusCNN module is not built, so this scenario can't run.")
exit(1)
run(True)