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forestevanescentElectronics - Devices

Nov 2, 2013 (3 years and 10 months ago)

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ABSTRACT

The

Mechatronic

Team

has

the

main

objective

for

the

development

project

to

make

the

electromechanical

integration

of

the

acquisition,

actuators

and

power

systems

of

an

Unmanned

Aerial

Vehicle

UAV,

and

also

do

the

respective

flight

test
.

Therefore,

it

is

required

to

make

a

careful

selection

by

engineering

criteria

of

both

the

vehicle

and

the

electronic

components

on

board

that

will

allow

us

to

achieve

the

established

objective

of

obtain

an

autonomous

vehicle

useful

for

multiple

applications
.


MECHATRONIC TEAM


Selection

of
components
:
vehicle
,
sensors

and
actuators


Design of electronic board: Control Power Servo Drives,
Acquisition of PWM signals and micro controller QE128.


Freescale

QE 128
Programming
. (
microcontroller
).


Flight test:
Data Capture of sensors and actuators.


Neuronal control and system identification.


Tasks

General
Mechatronic

Diagram

Diagram

of
the

Electronic

board
: “
Neurocopter

1.0”

Freescale

µController QE128 Programming


ADC interfaces.


Serial Communication interface. (
Tx

& Rx)


PWM modules: PWM signal generation, and capture of
PWM signals.


SPI protocol (Master


Slave): SD memory in SPI mode.


Flight algorithms.


Flight Test Diagram

Flight test: Real Time Data Capture of sensors and
actuators.

PWM and
sensors

Capture

Conversion

of
digitized

data
to

units

of
measurement

data

Kalman

Filter

System

ID

Neuro

controller:

MLP

network

model

is

used
.

The

design

of

the

NN

is

composed

by

9

values

at

the

input

layer,

10

activation

sigmoid

neurons

at

the

hidden

layer,

and

5

linear

neurons

at

the

output

layer
.

The

training

is

done

by

the

backpropagation

algorithm

of

the

NN

Matlab

toolbox
.








Inputs:

accelX
:
Linear Acceleration axis X (
m/s2)
.

accelY
:
Linear Acceleration axis
Y
( m/s2)
.

accelZ
:
Linear Acceleration axis
Z
( m/s2)
.

GyroX
: Angular
velocity

axis X (
°
/
sec
).

GyroY
: Angular
velocity

axis
Y
(
°
/
sec
)

.

GyroZ
: Angular
velocity

axis
Z
(
°
/
sec
)

.

AngX
:
Angle

X in
degrees

°
.

AngY
:
Angle

Y
in
degrees

°
.

Height
:
Height of the helicopter from the land in
centimeters.


Outputs:

u1:
% PWM
Duty

Cycle

Main

Rotor.

u2: % PWM
Duty

Cycle

Pitch Servo.

u3: % PWM
Duty

Cycle

Roll Servo (
Right

side
).

u4: % PWM
Duty

Cycle

Roll Servo
(
Left

side
)
.

u5: % PWM
Duty

Cycle

Yaw

Servo.











Neuro

controller for a MIMO system ID

Pitch servo:

Main

rotor
velocity
:

Training Results: Real PWM VS Neuronal PWM

Roll servo (
Right

side
):

Yaw

servo:

Roll servo (
Left

side
):

The

development

of

autonomous

scale

helicopters

responds

to

the

need

for

greater

flexibility,

agility

and

simplicity

of

operation
.


However,

they

present

highly

nonlinear

flight

dynamics

and

also

they

have

high

sensitivity

to

control

inputs

and

disturbances
.

If

we

add

the

fact

that

helicopters

present

different

characteristics

for

each

flight

mode,

the

development

and

implementation

of

an

intelligent

control

system

becomes

a

critical

factor

for

the

deployment

of

this

kind

of

air

vehicles
.


CONTROL

CONTROL STAGES



System

Identification


LQG
controller

design


Neural Network
design

DIAGRAM

a
x

a
y

a
z

p

q

r

h

HELICOPTER


Black Box


Input Data

Output
Data

System Identification

We

used

the

method

of

Linear

Regression

with

Least

Squares

using

QR

decomposition

to

approximate

the

real

system

to

a

transfer

function
.

T
ransfer function of order “m”:

Rewriting

the

equation

above

and

adding

an

error

term

representing

the

difference

between

the

real

system

and

approximate

system,

the

linear

regression

model

is

obtained
:



Output
Data

Available











Regression

Vector











Unknown

Parameter
s










s

Error
Term


According to the least squares sense, it is necessary to minimize:


A method of estimating the unknown parameters in a way to reduce the square
error is through Linear Regression with Least Mean Square Estimation
using
QR
decomposition

Knowing that:


The

matrix

can

be

separated

in

an

orthogonal

Q

matrix

and

in

an

upper

triangular

R

matrix

using

the

command

(QR)
.


Where ‘p’ is the number of unknown parameters


System Identification

LQG

LQE

(
Kalman

Filter
)
:

A
n

optimal

estimator

for

estimating

the

states

in

the

presence

of

AWGN

noise
.


LQR
:

An

optimal

regulator

supplied

by

estimated

states

(LQE)

which

are

taken

to

zero
.



LQE

LQR


Noise measured by
sensors


Exogenous disturbance


Component degradation



Linear Quadratic Gaussian
Controller (LQG)


Diagram of LQG
controller:


Optimal
Estimator
(LQE)

Kalman

Filter

Optimal
C
ontroller
(LQR)

+

Exogenous disturbance

LQG

Noise measured by sensors

Linear Quadratic Gaussian
Controller (LQG)

u

h
-

h
+

y
-

y

W
1

W
2

Configuration
:


Neural Network
Controller

The

neural

network

controller

tries

to

identify

the

controller

K

obtained

by

the

Linear

Quadratic

Regulator

LQR

method
.

It

is

important

to

notice

that

the

inputs

to

the

neural

network

are

the

outputs

of

the

Kalman

filter
.


X
O

Nonlinear
Neurons

Linear
Neurons

Learning
Algorithm:

The

algorithms

for

updating

the

weights

is

the

Backpropagation

which

involves

the

development

of

partial

derivatives
.


It

is

necessary

to

establish

a

Quadratic

Cost

Function

in

order

to

penalize

the

error

and

reduce

the

energy

that

is

coupled

to

the

system



Neural Network
Controller


Final
Diagram:


Optimal
Estimator
(LQE)

Kalman

Filter

+

Exogenous disturbance

LQG

Noise measured by sensors

W
1

W
2

Neural
Network


Neural Network
Controller

Computer vision is a field of artificial intelligence, which objective is to
program a computer in order to “understand” a scene or characteristics of a
certain image.

A common problem in airports is the presence of birds that blocks the aircraft
takeoff. Another common situation is the lack of a fast and movable
surveillance system. That’s why we propose an affordable, easy to use, modular
system with three operation modes.

ABSTRACT

General Diagram

Abstract


Tracking mode. The Neurocopter is able to follow a
certain object within its vision range


All weather vision.

In addition for the Tracking Mode
this Mode allows enhance the image for poor or
excessive light situations


Bird Detection. It allows us to identify bird in the
scene.

Tracking Mode

Acquisition

(Wireless camera)

Video enhancement

(space filters)

Color Selection


Color segmentation
(Euclidean distance)



Segmented Image
enhancement

(Morphological
Operations )

Representation and
Description

(Centroid and Trajectory
Calculation)

Visualization

Reference signals for
Control Team

Tracking Mode


We explore the following pre
-
processing algorithms:


Mean filter


Median filter


Morphological filters (Opening, Closing, Filling Holes
and Clear Border)





Tracking Mode


The segmentation algorithm was implemented in a
Nvidia

GTX 465 video card exploding its parallel
processing technology. It gave us a reduction of 55.7%
in time of execution.


Euclidean distance


All Weather Vision


This mode was achieved using the Gamma Correction which consists
in a non
-
linear adjust of the brightness or luminance on an image. For
the darkest pixels the brightness is highly increased while the
brightness for the clearest pixels is increased in an minor amount. As a
result more details are visible on the image.






All Weather Vision


The gamma correction was implemented on the GPU
because most of the operations include matrices, this
allows us to take advantage of the parallel processing.

Gamma
Night

Vision

= 0.9

Gamma
Sun

Block = 1.03


Time CPU


0.0159


Time GPU


0.0028


Reduction of 82
.4%


Bird
Detecction

Image
Acquisition

(Gray scale
conversion)

Pre
-
processing

(Median Filter)

Segmentation

(
Thresholding
)

Representation

(Morphological
Operations)

Description
(
Centroid
)

Thresholding
: It’s about defining a threshold which separates the objects
from the background. It is useful only if there is a clear difference between
the objects and the background of the scene.



Bird Detection


Threshold = 0.3


There is no need to use elaborated
algoritms

like the Otsu
algoritm



The

purpose

of

these

project

is

to

solve

the

communication

problems

and

to

ensure

the

control

of

the

helicopter

and

its

monitoring
.



Some

problems

may

happen

in

the

communication

system

due

to

limitations

like

electromagnetic

interference,

loss

of

communication

link,

errors

in

transmission

or

capture

of

wrong

information
.

ABSTRACT

DATALINK
-
ABSTRACT



The

principal

objectives

are
:


To

select

the

technology

for

the

communications

system

which

satisfied

to

the

required

specifications
.


To

design

a

software

when

can

monitoring

the

data

transmitted

(

altitude,

orientation

and

speed)


To

display

the

absolute

position

of

the

vehicle

through

the

GPS

scale


Datalink

Diagram




2. Datalink Down

1. Datalink Up

3.
TCP
Protocold

Control

(
Client
)

GCS

(Server)

ACTIVITIES


Choose

a

communication

device

and

configure

and

test

it
.


Simulate

data

acquisition

of

the

sensors

and

GPS

in

order

to

evaluate

the

software

developed
.


Display

information

received

from

the

helicopter

to

the

base

station
.

MONITORING BY GOOGLE EARTH

Start of
Google Earth

Extract
latitude,
longitude

Receive
GPS
data?

KML record

Show flight
and UAV
position

Finish?

End

No

Yes

No

Yes

SERIAL PORT COMMUNICATION

Start

Receive
VC
frame?

End

No

Configuration of
the serial port
parameters

Open serial port

Send ACK frame

Prepare to receive

Data?

State: Data received

Finish
data
sending
?

State: Finish

Close serial port

1

1

No

No

Yes

Yes

Yes

SERIAL PORT COMMUNICATION

Start

Receive
VC
frame?

End

No

Configuration of
the serial port
parameters

Open serial port

Prepare to receive

State: Send Data

Finish
data
sending
?

State: Finish

Close serial port

1

No

Send VC frame

1

Yes

Yes

XML AND KML


XML,

acronym

for

Extensible

Markup

Language

.

It’s

an

extensible

meta

tags

developed

by

the

World

Wide

Web

Consortium

(W
3
C)


XML

is

not

born

only

for

use

internet,

it

is

proposed

as

a

standard

for

exchanging

structured

information

between

different

platforms
.

It

can

be

used

in

databases,

text

editors,

spreadsheets

and

almost

anything

imaginable
.


KML

is

a

markup

language

and

its

used

to

represent

data

in

three

dimensions
.

KML PROGRAMMATION


KML
Programmation


<xml version

<
kml

xmlns
=“…”


<
Placemarks
>



<name>







</name>


<description>….. </description>



<point>




<coordinates>




Latitud,longitud,altura




</coordinates>



</point>


</
Placemarks
>

</
kml
>



DESIGN OF THE GCS

Conclusions
.


The

mathematical

analysis

allows

a

comprehensive

understanding

of

the

system

to

be

developed
:

Place

a

scale

helicopter

called


Neuro

Copter”

prototype

in

a

state

of

"Hover
.
"


Given

the

cost

numbers

and

physical

dimensions

of

the

prototype

to

implement,

was

determined

to

take

safety

measures

for

the

first

flight

test,

using

a

security

system

comprised

of

harness,

and

a

safe

landing
.

In

addition,

convenient

saw

the

acquisition

of

a

prototype

low
-
cost

training

in

comparison

to

the

final

model

for

the

respective

flight

test

and

flight

training
.


As

a

way

of

alleviating

the

computational

burden

of

the

Control

Team

and

the

Vision

Team

was

chosen

to

perform

each

of

the

two

processes

on

different

computers
.

We

created

a

client
-
server

application

TCP

/

IP

to

communicate

between

computers
.


The

GCS

is

an

important

tool

that

will

serve

as

interface

man

-

machine

for

controlling

the

UAV
.

This

software

will

change

in

real

time

between

each

of

the

navigation

modes

are

available
:

Collision

Avoidance,

Navigation

and

Waypoint

Day

/

Night

Vision
.


The

GCS

will

allow

real

time

viewing

of

both

the

position

of

the

UAV

and

its

speed,

angular

acceleration,

height

and

other

states
.

In

addition,

you

can

display

the

battery

status

is

making

contingency

alert

to

be

issued
.


The

image

processing

is

strongly

improved

by

the

use

of

parallel

processing

tecnology

provided

by

the

graffic

card
.


The

Linear

Regression

with

Least

Squares

using

QR

decomposition

is

more

efficient

that

using

pseudo
-
inverse

and,

also,

this

last

method

does

not

always

provides

a

consistent

solution

for

estimating

the

unknown

parameters
.


The

Linear

Quadratic

Gaussian

Controller

allows

the

track

of

all

the

states

even

if

they

are

contaminated

by

AWGN

noise
.


The

estimated

level

of

system

identification

using

neural

networks

using

the

MLP

model

is

vastly

superior

to

the

estimation

using

the

autoregressive

model

ARX
.


Neural

networks

are

highly

recommended

for

system

identification

and

development

of

controllers

for

nonlinear

systems
.


For

the

training

is

recommended

to

leave

20
%

of

the

information

to

make

the

neural

network

able

to

generalize
.


Once,

the

neuro

controller

is

trained,

it

is

necessary

to

validate

the

neural

network
.

For

example,

feed

to

the

system

with

different

values

to

the

training

patterns

and

verify

that

the

output

is

consistent
.


Leave

a

small

margin

of

error

on

the

training

goal

(non
-
zero

error)

in

order

to

allow

the

network

to

generalize
.


For

the

modeling

of

the

system

take

advantage

of

neural

networks,

and

decompose

the

system

into

simple

elements
.


The

torque

of

the

helicopter

is

1
,

0115

Kg
.
m
.