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rm_tp copy.py
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import numpy as np
from copy import deepcopy
from scipy.io import loadmat
from tracker import Tracker, Planner, Relative
class RmTracker(Tracker):
def __init__(self):
self.size = 5
self.dt = 0.01
def dynamics(self, x, u):
new_x = deepcopy(x)
new_x[0] += x[1] * self.dt#(x[1]*np.cos(x[4]) - x[3]*np.sin(x[4])) * self.dt
new_x[1] += u[0] * self.dt
new_x[2] += x[3] * self.dt#(x[1]*np.sin(x[4]) + x[3]*np.cos(x[4])) * self.dt
new_x[3] += u[1] * self.dt
new_x[4] += u[2] * self.dt
return new_x
class RmPlanner(Planner):
def __init__(self):
self.size = 3
def dynamics(self, x, u):
new_x = deepcopy(x)
new_x[0] += u[0]*np.cos(x[2]) - u[1]*np.sin(x[2])
new_x[1] += u[0]*np.sin(x[2]) + u[1]*np.cos(x[2])
new_x[2] += u[2]
return new_x
def project(self, s):
p = [s[0], s[2], s[4]]
return p
def control(self, p):
x = p[0] + 2.0 * 0.01
y = p[1] + 2.0 * 0.01
theta = p[2] + 4.16 * 0.01
return [x, y, theta]
class RmRelative(Relative):
def __init__(self):
self.size = 5
self.uMax = np.array([10.0, 10.0, 8.32, 2.0, 3.0, 4.16])
self.uMin = -self.uMax
def state(self, s, p):
r = deepcopy(s)
r[0] -= p[0]
r[2] -= p[1]
r[4] -= p[2]
return r
def dynamics(self, r, u, d):
rnext = deepcopy(r)
rnext = np.clip(rnext, [-5, -2, -5, -3, -np.pi], [5, 2, 5, 3, np.pi])
rnext[0] += x[1] - u[3]*np.cos(x[4]) + u[4]*np.sin(x[4]) + d[0]
rnext[1] += u[0]
rnext[2] += x[3] - u[3]*np.sin(x[4]) - u[4]*np.cos(x[4]) + d[1]
rnext[3] += u[1]
rnext[4] += u[2] - u[5] + d[2]
return rnext
def optControl(self, deriv, r, x):
# x-subsystem
ax_deriv = deriv[1]
uOpt_0 = (ax_deriv>=0)*self.uMin[0] + (ax_deriv<0)*self.uMin[0]
bx_x_deriv = -deriv[0]*np.cos(x[4])
uOpt_3_x = (bx_x_deriv>=0)*self.uMax[3] + (bx_x_deriv<0)*self.uMin[3]
by_x_deriv = deriv[0]*np.sin(x[4])
uOpt_4_x = (by_x_deriv>=0)*self.uMax[4] + (by_x_deriv<0)*self.uMin[4]
# y-subsystem
ay_deriv = deriv[3]
uOpt_1 = (ay_deriv>=0)*self.uMin[1] + (ay_deriv<0)*self.uMax[1]
bx_y_deriv = -deriv[2]*np.sin(x[4])
uOpt_3_y = (bx_y_deriv>=0)*self.uMax[3] + (bx_y_deriv<0)*self.uMin[3]
by_y_deriv = -deriv[2]*np.cos(x[4])
uOpt_4_y = (by_y_deriv>=0)*self.uMax[4] + (by_y_deriv<0)*self.uMin[4]
# pessimistic system
w_deriv = deriv[4]
uOpt_2 = (w_deriv>=0)*self.uMin[2] + (w_deriv<0)*self.uMax[2]
btheta_deriv = -deriv[4]
uOpt_5 = (btheta_deriv>=0)*self.uMax[5] + (btheta_deriv<0)*self.uMin[5]
# Calculate opt ctrl
uopt = [
deriv[0] * ( x[1] - uOpt_3_x*np.cos(x[4]) + uOpt_4_x*np.sin(x[4]) ),
deriv[1] * uOpt_0,
deriv[2] * ( x[3] - uOpt_3_y*np.sin(x[4]) - uOpt_4_y*np.cos(x[4]) ),
deriv[3] * uOpt_1,
deriv[4] * ( uOpt_2 - uOpt_5 )
]
return uopt
class Reach():
def __init__(self):
matlabf = "./RMAI_g_dt01_t5_medium_quadratic.mat"
fst = loadmat(matlabf)
eps = 0.1
self.eb = max(fst['TEB_X'], fst['TEB_Y']) + eps
self.vf_X = np.array(fst['data_X'])
self.vf_Y = np.array(fst['data_Y'])
self.vf_dX = fst['derivX']
self.vf_dY = fst['derivY']
def to_grid_index(self, r):
r = deepcopy(r)
r[0] = int(((r[0] + 5) / 10.0) * 60)
r[1] = int(((r[1] + 2) / 4.0) * 60)
r[2] = int(((r[2] + 5) / 10.0) * 60)
r[3] = int(((r[3] + 3) / 6.0) * 60)
r[4] = int(((r[4] + np.pi) / (2*np.pi)) * 60)
return r
def get_vf_dW(self, r):
x_w = self.vf_X[r[0], r[1], r[4]]
y_w = self.vf_Y[r[2], r[3], r[4]]
if x_w >= y_w:
dw = self.vf_dX[2][0][r[0], r[1], r[4]]
else:
dw = self.vf_dY[2][0][r[2], r[3], r[4]]
return dw
def check_on_boundary(self, r):
r_int = [int(i) for i in r]
vf_X_eb = self.vf_X[r_int[0], r_int[1], r_int[4]]
vf_Y_eb = self.vf_Y[r_int[2], r_int[3], r_int[4]]
vf_eb = max(vf_X_eb, vf_Y_eb)
if vf_eb >= self.eb:
return True
else:
return False
def control(self, s, pnext):
dx = pnext[0] - s[0]
dy = pnext[1] - s[1]
dw = pnext[2] - s[2]
ax = 5.0 * dx
ay = 5.0 * dy
return [ax, ay, dw]
def get_derivs(self, r):
r_int = [int(i) for i in r]
deriv = [
self.vf_dX[0][0][r_int[0], r_int[1], r_int[4]],
self.vf_dX[1][0][r_int[0], r_int[1], r_int[4]],
self.vf_dY[0][0][r_int[2], r_int[3], r_int[4]],
self.vf_dY[1][0][r_int[2], r_int[3], r_int[4]],
self.get_vf_dW(r_int)
]
return deriv