def createAUV(X0):
auv = AUV(T, delta, X0, DW, U)
for i in range(0, accuracy.size):
ph = PhiBounds[i, :]
th = ThetaBounds[i, :]
PhiGrad = np.append(np.arange(ph[0], ph[1], (ph[1] - ph[0]) / NBeams),
ph[1])
ThetaGrad = np.append(
np.arange(th[0], th[1], (th[1] - th[0]) / NBeams), th[1])
auv.addsensor(accuracy[i],
PhiGrad / 180.0 * np.pi,
ThetaGrad / 180.0 * np.pi,
seabed,
estimateslope=True)
return auv
def createAUV(X0):
auv = AUV(T, delta, X0, DW, U)
# if the prediction is intended to be by virtue of the system, then the following loop has to be commented
# for i in range(0, accuracy.size):
# ph = PhiBounds[i,:]
# th = ThetaBounds[i,:]
# PhiGrad = np.append(np.arange(ph[0], ph[1], (ph[1] - ph[0]) / NBeams), ph[1])
# ThetaGrad = np.append(np.arange(th[0], th[1], (th[1] - th[0]) / NBeams), th[1])
# auv.addsensor(accuracy[i], PhiGrad / 180.0 * np.pi, ThetaGrad / 180.0 * np.pi, seabed, estimateslope = True)
return auv
X0 = [
0.001 + 0.1 * np.random.normal(0, 1),
-0.0002 + 0.1 * np.random.normal(0, 1),
-10.0003 + 0.1 * np.random.normal(0, 1)
]
#DW = [0.05,0.05,0.05]
DW = [0.0, 0.0, 0.0]
U = lambda t: np.pi * np.array([
1 / 100.0 * np.cos(1.0 * np.pi * t / T), 1 / 3.0 * np.cos(4.0 * np.pi * t /
T)
])
v = 2.0 # 5.0
seabed = Profile()
estimateslope = False
auv = AUV(T, delta, X0, DW, U, v)
test = TestEnvironment(T, delta, NBeams, accuracy, PhiBounds, ThetaBounds, auv,
seabed, estimateslope)
test.run()
test.plotspeed([0, 0, 0],
'D:\\projects.git\\NavigationResearch\\results\\slope_known\\')
test.plot3Dtrajectory(
[0], 'D:\\projects.git\\NavigationResearch\\results\\slope_known\\')
test.plottrajectory(
'D:\\projects.git\\NavigationResearch\\results\\slope_known\\')
estimateslope = True
auv = AUV(T, delta, X0, DW, U, v)
test = TestEnvironment(T, delta, NBeams, accuracy, PhiBounds, ThetaBounds, auv,
seabed, estimateslope)
test.run()
test.plotspeed(