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UuvModel.py
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UuvModel.py
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#!/usr/bin/env python
# -*- encoding: utf-8 -*-
'''
@File : UuvModel.py
@Time : Mon Aug 17 2020 10:15:02
@Author : xiaobo
@Contact : [email protected]
'''
# Copyright (C)
#
# This file is part of QuadrotorFly
#
# GWpy is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# GWpy is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with GWpy. If not, see <http://www.gnu.org/licenses/>.
# here put the import lib
import numpy as np
import MemoryStore
'''
********************************************************************************************************
**-------------------------------------------------------------------------------------------------------
** Compiler : python 3.6
** Module Name: UuvModel.py
** Module Date: 2020-08-17
** Module Auth: xiaobo
** Version : V0.1
** Description: create the module
**-------------------------------------------------------------------------------------------------------
** Reversion :
** Modified By:
** Date :
** Content :
** Notes :
********************************************************************************************************/
'''
# definition of key constant
D2R = np.pi / 180
state_dim = 12
action_dim = 4
state_bound = np.array([10, 10, 10, 5, 5, 5, 80 * D2R, 80 * D2R, 180 * D2R, 100 * D2R, 100 * D2R, 100 * D2R])
action_bound = np.array([1, 1, 1, 1])
def rk4(func, x0, action, h):
"""Runge Kutta 4 order update function
:param func: system dynamic
:param x0: system state
:param action: control input
:param h: time of sample
:return: state of next time
"""
k1 = func(x0, action)
k2 = func(x0 + h * k1 / 2, action)
k3 = func(x0 + h * k2 / 2, action)
k4 = func(x0 + h * k3, action)
# print(test)
# print('rk4 debug: ', trans(k1), trans(k2), trans(k3), trans(k4))
x1 = x0 + h * (k1 + 2 * k2 + 2 * k3 + k4) / 6
return x1
def trans(xx):
s2 = np.zeros(12)
s2[0:3] = xx[3:6]
s2[3:6] = xx[9:12]
s2[6:9] = xx[6:9]
s2[9:12] = xx[0:3]
return s2
class UuvParas(object):
"""Define the parameters of quadrotor model
"""
def __init__(self, g=9.8, tim_sample=0.01,
uuv_b=14671, uuv_m=1840, uuv_dis_c=np.array([0.02, -0.005, 0.008]), uuv_j=np.array([289.3, 6771.8, 6771.8]), uuv_lu=1019.2, uuv_s=0.224, uuv_l=7.738,
uuv_cx=0.141, uuv_mx_beta=0.00152, uuv_mx_dr=-0.000319, uuv_mx_dd=-0.0812, uuv_mx_wx=-0.0044, uuv_mx_wy=0.0008, uuv_mx_xp=0,
uuv_cy_alfa=2.32, uuv_cy_de=0.51, uuv_cy_wz=1.17, uuv_my_beta=0.69, uuv_my_dr=-0.11, uuv_my_wx=0, uuv_my_wy=-0.61,
uuv_cz_beta=-2.32, uuv_cz_dr=-0.2, uuv_cz_wy=-1.17, uuv_mz_alpha=0.69, uuv_mz_de=-0.28, uuv_mz_wz=-0.61,
uuv_add_m_k11=0.0222, uuv_add_m_k22=1.1096, uuv_add_m_K44=0.1406, uuv_add_m_k55=3.8129, uuv_add_m_k26=-0.363):
"""init the quadrotor parameters
These parameters are able to be estimation in web(https://flyeval.com/) if you do not have a real UAV.
common parameters:
-g : N/kg, acceleration gravity
-tim_sample : s, sample time of system
uuv:
-uuv_b : N, floating force of uuv
-uuv_m : kg, the mass of quadrotor
-uuv_dis_c : m, distance from center of mass to center of floting
-uuv_j : kg.m^2 the central principal moments of inertia of UAV in x,y,z
-uuv_lu : kg/(m^3) the density of water
-uuv_s : m^2 the max area of the uuv in direction x
-uuv_l : m the length of the uuv
-uuv_cx : the factor between drag-force and uuv_s
-uuv_mx_beta: the factor between roll-force and beta
-uuv_mx_dr: the factor between roll-force and delta_r
-uuv_mx_dd: the factor between roll-force and delta_d
-uuv_mx_wx: the factor between roll-force and omega_x
-uuv_mx_wy: the factor between roll-force and omega_y
-uuv_mx_xp:
-uuv_cy_alfa: the factor between up-force and alpha
-uuv_cy_de: the factor between up-force and delta_e
-uuv_cy_wz: the factor between up-force and omega_z
-uuv_my_beta: the factor between yaw-force and beta
-uuv_my_dr: the factor between yaw-force and delta_r
-uuv_my_wx:
-uuv_my_wy: the factor between yaw-force and omega_y
-uuv_cz_beta: the factor between right-force and beta
-uuv_cz_dr: the factor between right-force and delta_r
-uuv_cz_wy: the factor between right-force and omega_y
-uuv_mz_alpha: the factor between pitch-force and alpha
-uuv_mz_de: the factor between pitch-force and delta_e
-uuv_mz_wz: the factor between pitch-force and omega_z
-uuv_add_m_k11: the factor between added mass in x
-uuv_add_m_k22: the factor between added mass in z
-uuv_add_m_K44: the factor between added mass in roll
-uuv_add_m_k55: the factor between added mass in pitch
-uuv_add_m_k26: the factor between added mass in
"""
self.ts = tim_sample
self.g = g
self.B = uuv_b
self.G = uuv_m * g # Gravity of uuv
self.M = uuv_m
self.Dis_c = uuv_dis_c
self.J = uuv_j
self.Lu = uuv_lu
self.S = uuv_s
self.L = uuv_l
self.Cx = uuv_cx
self.MxBeta = uuv_mx_beta
self.MxDr = uuv_mx_dr
self.MxDd = uuv_mx_dd
self.MxWx = uuv_mx_wx
self.MxWy = uuv_mx_wy
self.MxP = 0
self.CyAlpha = uuv_cy_alfa
self.CyDe = uuv_cy_de
self.CyWz = uuv_cy_wz
self.MyBeta = uuv_my_beta
self.MyDr = uuv_my_dr
self.MyWy = uuv_my_wy
self.CzBeta = uuv_cz_beta
self.CzDr = uuv_cz_dr
self.CzWy = uuv_cz_wy
self.MzAlpha = uuv_mz_alpha
self.MzDe = uuv_mz_de
self.MzWz = uuv_mz_wz
self.AddMk11 = uuv_add_m_k11
self.AddMk22 = uuv_add_m_k22
self.AddMk33 = uuv_add_m_k22
self.AddMk44 = uuv_add_m_K44
self.AddMk55 = uuv_add_m_k55
self.AddMk66 = uuv_add_m_k55
self.AddMk26 = uuv_add_m_k26
self.AddMk35 = -uuv_add_m_k26
V = self.B / (self.Lu * self.g)
self.AddML11 = self.AddMk11 * self.Lu * V
self.AddML22 = self.AddMk22 * self.Lu * V
self.AddML33 = self.AddMk33 * self.Lu * V
self.AddML44 = self.AddMk44 * self.Lu * np.power(V, 5./3)
self.AddML55 = self.AddMk55 * self.Lu * np.power(V, 5./3)
self.AddML66 = self.AddMk66 * self.Lu * np.power(V, 5./3)
self.AddML26 = self.AddMk26 * self.Lu * np.power(V, 4./3)
self.AddML35 = self.AddMk35 * self.Lu * np.power(V, 4./3)
class UuvModel(object):
def __init__(self, uuv_para: UuvParas):
"""init a uuv"""
self.agtPara = uuv_para # the para of agent
# states of uuv
# -position, m
self.position = np.array([0, 0, 0])
# -velocity, m/s
self.velocity = np.array([0, 0, 0])
# -attitude, rad
self.attitude = np.array([0, 0, 0])
# -angular, rad/s
self.angular = np.array([0, 0, 0])
# accelerate, m/(s^2)
self.acc = np.zeros(3)
# time control, s
self.__ts = 0
def dynamic_basic(self, state, action, debug=False):
""" calculate /dot(state) = f(state) + u(state)
This function will be executed many times during simulation, so high performance is necessary.
:param state:
0 1 2 3 4 5
p_x p_y p_z v_x v_y v_z
6 7 8 9 10 11
roll pitch yaw v_roll v_pitch v_yaw
:param action: u1(sum of thrust), u2(torque for roll), u3(pitch), u4(yaw)
:return: derivatives of state inclfrom bokeh.plotting import figure
"""
dot_state = np.zeros([12])
if debug:
test = dict()
p = self.agtPara
a = np.array([
[p.M + p.AddML11, 0, 0, 0, p.M * p.Dis_c[2], -p.M * p.Dis_c[1]],
[0, p.M + p.AddML22, 0, -p.M * p.Dis_c[2], 0, p.M * p.Dis_c[0] + p.AddML26],
[0, 0, p.M + p.AddML33, p.M * p.Dis_c[1], p.AddML35 - p.M * p.Dis_c[0], 0],
[0, -p.M * p.Dis_c[2], p.M * p.Dis_c[1], p.J[0] + p.AddML44, 0, 0],
[p.M * p.Dis_c[2], 0, p.AddML35 - p.M * p.Dis_c[0], 0, p.J[1] + p.AddML55, 0],
[-p.M * p.Dis_c[1], p.M * p.Dis_c[0] + p.AddML26, 0, 0, 0, p.J[2] + p.AddML66]
])
de = action[0]
dr = action[1]
dd = action[2]
tl = action[3]
# the sequence here is different from the book "yu lei hang xing xue"
vx = state[3]
vy = state[4]
vz = state[5]
wx = state[9]
wy = state[10]
wz = state[11]
pesi = state[8]
sita = state[7]
fai = state[6]
v2 = vx * vx + vy * vy + vz * vz
# speed
v = np.sqrt(v2)
# attack angle
alfa = np.arctan(-vy / vx)
vxy = np.sqrt(vx * vx + vy * vy)
# side angle
beta = np.arctan(vz / vxy)
salfa = np.sin(alfa)
calfa = np.cos(alfa)
sbeta = np.sin(beta)
cbeta = np.cos(beta)
spesi = np.sin(pesi)
cpesi = np.cos(pesi)
ssita = np.sin(sita)
csita = np.cos(sita)
sfai = np.sin(fai)
cfai = np.cos(fai)
cvb = np.array([
[calfa * cbeta, salfa, -calfa * sbeta],
[-salfa * cbeta, calfa, salfa * sbeta],
[sbeta, 0, cbeta]
])
ceb = np.array([
[csita * cpesi, ssita, -csita * spesi],
[-ssita * cpesi * cfai + spesi * sfai, csita * cfai, ssita * spesi * cfai + cpesi * sfai],
[ssita * cpesi * sfai + spesi * cfai, -csita * sfai, -ssita * spesi * sfai + cpesi * cfai]
])
if debug:
test['cvb'] = cvb
test['ceb'] = ceb
# print('dynamic test 1: -----------------------')
# print('cvb is ', cvb)
# print('ceb is ', ceb)
fs_v = np.array([-p.Cx * 0.5 * p.Lu * v2 * p.S,
p.CyAlpha * 0.5 * p.Lu * v2 * p.S * alfa + p.CyWz * 0.5 * p.Lu * p.S * p.L * v * wz + p.CyDe * 0.5 * p.Lu * v2 * p.S * de,
p.CzBeta * 0.5 * p.Lu * v2 * p.S * beta + p.CzWy * 0.5 * p.Lu * p.S * p.L * v * wy + p.CzDr * 0.5 * p.Lu * v2 * p.S * dr
])
# force
fs = cvb.dot(fs_v)
# torque
ms = np.array([
p.MxBeta * 0.5 * p.Lu * p.S * p.L * v2 * beta + p.MxWx * 0.5 * p.Lu * p.S * (np.square(p.L)) * wx * v
+ p.MxDr * 0.5 * p.Lu * p.S * p.L * v2 * dr + p.MxDd * 0.5 * p.Lu * p.S * p.L * v2 * dd + p.MxP * v2,
p.MyBeta * 0.5 * p.Lu * p.S * p.L * v2 * beta + p.MyWy * 0.5 * p.Lu * p.S * (np.square(p.L)) * wy * v + p.MyDr * 0.5 * p.Lu * p.S * p.L * v2 * dr,
p.MzAlpha * 0.5 * p.Lu * p.S * p.L * v2 * alfa + p.MzWz * 0.5 * p.Lu * p.S * (np.square(p.L)) * wz * v + p.MzDe * 0.5 * p.Lu * p.S * p.L * v2 * de
])
# floating force
fg = ceb.dot(np.array([0, p.B - p.G, 0]))
if debug:
test['fs'] = fs
test['ms'] = ms
test['fg'] = fg
mg_m = np.array([
[0, -p.Dis_c[2], p.Dis_c[1]],
[p.Dis_c[2], 0, -p.Dis_c[0]],
[-p.Dis_c[1], p.Dis_c[0], 0]
])
# gravity
mg = mg_m.dot(ceb.dot(np.array([0, -p.G, 0])))
ft = np.array([
[-p.M * (vz * wy - vy * wz + p.Dis_c[1] * wx * wy + p.Dis_c[2] * wx * wz - p.Dis_c[0] * (np.square(wy) + np.square(wz)))],
[-p.M * (vx * wz - vz * wx + p.Dis_c[2] * wy * wz + p.Dis_c[0] * wy * wx - p.Dis_c[1] * (np.square(wz) + np.square(wx)))],
[-p.M * (vy * wx - vx * wy + p.Dis_c[0] * wz * wx + p.Dis_c[1] * wz * wy - p.Dis_c[2] * (np.square(wx) + np.square(wy)))]
])
mt = np.array([
[-p.M * (p.Dis_c[1] * (vy * wx - vx * wy) + p.Dis_c[2] * (vz * wx - vx * wz)) - (p.J[2] - p.J[1]) * wy * wz],
[-p.M * (p.Dis_c[2] * (vz * wy - vy * wz) + p.Dis_c[0] * (vx * wy - vy * wx)) - (p.J[0] - p.J[2]) * wz * wx],
[-p.M * (p.Dis_c[0] * (vx * wz - vz * wx) + p.Dis_c[1] * (vy * wz - vz * wy)) - (p.J[1] - p.J[0]) * wx * wy]
])
if debug:
test['mg'] = mg
test['ft'] = ft
test['mt'] = mt
# print('dynamic test 3: -----------------------')
# print('mg is ', mg)
# print('ft is ', ft)
# print('mt is ', mt)
ftl = np.array([tl, 0, 0])
f = np.hstack([fs.reshape([3]) + fg.reshape([3]) + ft.reshape([3]) + ftl.reshape([3]), ms.reshape([3]) + mg.reshape([3]) + mt.reshape([3])])
df_speed = np.linalg.inv(a).dot(f)
angle_rot = np.array([
[0, cfai / csita, -sfai / csita],
[0, sfai, cfai],
[1, -cfai * np.tan(sita), sfai * np.tan(sita)]
])
df_2 = angle_rot.dot(np.array([wx, wy, wz]))
df_3 = ceb.dot([vx, vy, vz])
if debug:
test['f'] = f
test['a'] = a
test['df_speed'] = df_speed
test['df_angle'] = df_2
dot_state[0:3] = df_3
dot_state[3:6] = df_speed[0:3]
dot_state[6:9] = df_2
dot_state[9:12] = df_speed[3:6]
if debug:
return dot_state, test
else:
return dot_state
def observe(self):
"""out put the system state, with sensor system or without sensor system"""
# if self.simPara.enableSensorSys:
# sensor_data = dict()
# for index, sensor in enumerate(self.sensorList):
# if isinstance(sensor, SensorBase.SensorBase):
# # name = str(index) + '-' + sensor.get_name()
# name = sensor.get_name()
# sensor_data.update({name: sensor.observe()})
# return sensor_data
# else:
return np.hstack([self.position, self.velocity, self.attitude, self.angular])
def get_reward(self):
reward = np.sum(np.square(self.position)) / 8 + np.sum(np.square(self.velocity)) / 20 \
+ np.sum(np.square(self.attitude)) / 3 + np.sum(np.square(self.angular)) / 10
return reward
def step(self, action):
self.__ts += self.agtPara.ts
# # 1.1 Actuator model, calculate the thrust and torque
# thrusts, toques = self.actuator.step(action)
# # 1.2 Calculate the force distribute according to 'x' type or '+' type, assum '+' type
# forces = self.rotor_distribute_dynamic(thrusts, toques)
# 1.3 Basic model, calculate the basic model, the u need to be given directly in test-mode for Matlab
state_temp = np.hstack([self.position, self.velocity, self.attitude, self.angular])
state_next = rk4(self.dynamic_basic, state_temp, action, self.agtPara.ts)
[self.position, self.velocity, self.attitude, self.angular] = np.split(state_next, 4)
# calculate the accelerate
state_dot = self.dynamic_basic(state_temp, action)
self.acc = state_dot[3:6]
# 2. Calculate Sensor sensor model
# if self.simPara.enableSensorSys:
# for index, sensor in enumerate(self.sensorList):
# if isinstance(sensor, SensorBase.SensorBase):
# sensor.update(np.hstack([state_next, self.acc]), self.__ts)
ob = self.observe()
# 3. Check whether finish (failed or completed)
finish_flag = False#self.is_finished()
# 4. Calculate a reference reward
reward = self.get_reward()
return ob, reward, finish_flag
if __name__ == '__main__':
" used for testing this module"
testFlag = 3
if testFlag == 1:
# test case 1
# 使用简单的原始数据测试
para = UuvParas()
agent = UuvModel(para)
xi = np.array([0, 0, 0, 10, 0, 0,
0, 0, 0, 0, 0, 0])
a0 = np.array([ 0, 0, 0, 10672])
dot_state, test = agent.dynamic_basic(xi, a0 ,debug=True)
## sec 1 ##############################################################
r_cvb = np.diag(np.ones(3))
r_ceb = np.diag(np.ones(3))
## sec 2 ##############################################################
r_fs = np.array([-1.6095, 0, 0]) * 1000
r_ms = np.array([0, 0, 0])
r_fg = np.array([0, -3361, 0])
## sec 3 ##############################################################
r_mg = np.array([144.2560, 0, -360.6400])
r_ft = np.array([0, 0, 0])
r_mt = np.array([0, 0, 0])
## sec 4 ##############################################################
r_f = 1.0e+04 * np.array([1.2282, -0.3361, 0, 0.0144, 0, -0.0361])
r_a = 1.0e+04 * np.array([
[0.1873, 0, 0, 0, 0.0015, 0.0009],
[ 0, 0.3501, 0, -0.0015, 0, -0.0581],
[ 0, 0, 0.3501, -0.0009, 0.0581, 0],
[ 0, -0.0015, -0.0009, 0.0561, 0, 0],
[0.0015, 0, 0.0581, 0, 1.4148, 0],
[0.0009, -0.0581, 0, 0, 0, 1.4148]
]),
r_df_speed = np.array([6.5567, -0.9706, 0.0018, 0.2316, -0.0069, -0.0696])
r_df_angle = np.array([0, 0, 0])
print('sec 1: ------------------------------------------------------------')
print('cvb is ', test['cvb'], '. should be ', r_cvb)
print('ceb is ', test['ceb'], '. should be ', r_ceb)
print('sec 2: ------------------------------------------------------------')
print('fs is ', test['fs'], '. should be ', r_fs)
print('ms is ', test['ms'], '. should be ', r_ms)
print('fg is ', test['fg'], '. should be ', r_fg)
print('sec 3: ------------------------------------------------------------')
print('mg is ', test['mg'], '. should be ', r_mg)
print('ft is ', test['ft'], '. should be ', r_ft)
print('mt is ', test['mt'], '. should be ', r_mt)
print('sec 4 -------------------------------------------------------------')
print('f is ', test['f'], '. should be ', r_f)
print('a is ', test['a'], '. should be ', r_a)
print('df_speed is ', test['df_speed'], '. should be ', r_df_speed)
print('df_angle is ', test['df_angle'], '. should be ', r_df_angle)
print('result is ', trans(dot_state))
elif testFlag == 2:
# import matplotlib.pyplot as plt
para = UuvParas()
agent = UuvModel(para)
s2 = np.zeros(12)
agent.velocity[0] = 10
xi = np.array([0, 0, 0, 10, 0, 0,
0, 0, 0, 0, 0, 0])
a0 = np.array([ 0, 0, 0, 10672])
# record = MemoryStore.DataRecord()
# record.clear()
step_cnt = 0
action2 = np.zeros(4)
action2[3] = 10672
print('action: ', action2)
agent.step(action2)
s1 = agent.observe()
s2[0:3] = s1[3:6]
s2[3:6] = s1[9:12]
s2[6:9] = s1[6:9]
s2[9:12] = s1[3:6]
print('1', s2)
elif testFlag == 3:
import matplotlib.pyplot as plt
para = UuvParas()
agent = UuvModel(para)
agent.velocity[0] = 10
xi = np.array([0, 0, 0, 10, 0, 0,
0, 0, 0, 0, 0, 0])
a0 = np.array([ 0, 0, 0, 10672])
record = MemoryStore.DataRecord()
record.clear()
step_cnt = 0
for i in range(2000):
ref = np.array([0., 0., 1., 0.])
stateTemp = agent.observe()
print(stateTemp)
action2 = np.zeros(4)
action2[3] = 10672
# action2, oil = quad1.get_controller_pid(stateTemp, ref)
print('action: ', action2)
# action2 = np.clip(action2, 0.1, 0.9)
agent.step(action2)
record.buffer_append((stateTemp, action2))
step_cnt = step_cnt + 1
record.episode_append()
data = record.get_episode_buffer()
bs = data[0]
ba = data[1]
t = range(0, record.count)
# mpl.style.use('seaborn')
fig1 = plt.figure(1)
plt.clf()
plt.subplot(3, 1, 1)
plt.plot(t, bs[t, 6] / D2R, label='roll')
plt.plot(t, bs[t, 7] / D2R, label='pitch')
plt.plot(t, bs[t, 8] / D2R, label='yaw')
plt.ylabel('Attitude $(\circ)$', fontsize=15)
plt.legend(fontsize=15, bbox_to_anchor=(1, 1.05))
plt.subplot(3, 1, 2)
plt.plot(t, bs[t, 4], label='y')
plt.plot(t, bs[t, 5], label='z')
plt.ylabel('Position (m)', fontsize=15)
plt.legend(fontsize=15, bbox_to_anchor=(1, 1.05))
plt.subplot(3, 1, 3)
plt.plot(t, bs[t, 3], label='x')
plt.ylabel('Forward (m)', fontsize=15)
plt.legend(fontsize=15, bbox_to_anchor=(1, 1.05))
plt.show()