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3
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W
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[
6
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.
An
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t
im
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o
f
g
en
er
atin
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th
e
co
n
tr
o
ller
[
11
-
1
3
]
.
I
n
c
r
e
a
s
i
n
g
t
h
e
n
u
m
b
e
r
o
f
m
o
ti
o
n
s
a
v
a
i
la
b
l
e
o
n
a
n
W
MR
c
o
n
t
r
o
l
l
e
r
is
n
o
t
a
s
i
m
p
le
t
as
k
,
s
i
n
c
e
i
n
c
r
e
a
s
i
n
g
t
h
e
r
o
b
u
s
t
n
e
s
s
o
f
t
h
e
c
o
n
t
r
o
ll
er
r
e
d
u
c
e
s
t
h
e
r
o
b
o
t'
s
o
p
e
r
at
i
n
g
s
p
e
e
d
w
h
e
n
p
e
r
f
o
r
m
i
n
g
a
t
as
k
.
T
h
e
r
e
f
o
r
e
,
m
o
t
i
o
n
a
n
d
c
o
n
t
r
o
l
s
c
h
e
m
e
s
h
a
v
e
b
ee
n
w
i
d
e
l
y
s
t
u
d
i
e
d
a
n
d
t
h
e
r
e
a
r
e
r
e
m
o
t
e
o
r
l
o
c
al
i
m
p
le
m
e
n
t
at
i
o
n
a
l
g
o
r
i
t
h
m
s
f
o
r
o
p
e
r
a
t
i
n
g
a
n
W
MR
.
S
o
m
e
o
f
t
h
e
m
o
s
t
u
s
e
d
a
l
g
o
r
it
h
m
s
c
u
r
r
e
n
t
ly
a
r
e
t
h
e
r
o
u
t
e
m
a
p
p
i
n
g
a
l
g
o
r
i
t
h
m
s
,
m
a
c
h
i
n
e
v
i
s
i
o
n
a
l
g
o
r
i
t
h
m
s
a
n
d
l
o
c
a
l
o
p
e
r
a
t
i
o
n
a
l
g
o
r
i
t
h
m
s
.
F
i
r
s
t
,
t
h
e
r
e
a
r
e
t
h
e
r
o
u
t
i
n
g
a
l
g
o
r
i
t
h
m
s
t
h
a
t
f
r
o
m
a
d
e
f
i
n
e
d
e
n
v
i
r
o
n
m
e
n
t
c
r
e
a
t
e
a
s
e
q
u
e
n
c
e
o
f
i
n
s
t
r
u
c
ti
o
n
s
t
h
a
t
a
r
e
s
e
n
t
t
o
t
h
e
r
o
b
o
t
,
t
o
g
o
f
r
o
m
o
n
e
p
l
a
c
e
t
o
a
n
o
t
h
e
r
.
I
n
s
e
c
o
n
d
p
l
a
c
e
,
t
h
e
r
e
a
r
e
t
h
e
m
a
c
h
i
n
e
v
is
i
o
n
a
l
g
o
r
ith
m
s
t
h
a
t
b
y
m
e
a
n
s
o
f
a
n
i
m
ag
e
p
r
o
c
e
s
s
i
n
g
al
g
o
r
i
t
h
m
d
e
t
e
r
m
i
n
e
t
h
e
l
o
c
a
ti
o
n
o
f
o
b
s
t
a
c
l
es
s
o
t
h
a
t
t
h
e
r
o
b
o
t
a
v
o
id
s
t
h
e
m
wi
t
h
t
h
e
a
d
v
a
n
t
a
g
e
t
h
at
t
h
e
e
n
v
i
r
o
n
m
e
n
t
c
a
n
b
e
p
a
r
t
i
al
l
y
k
n
o
w
n
.
F
i
n
al
l
y
,
t
h
e
l
o
c
a
l
o
p
e
r
a
t
i
o
n
a
l
g
o
r
i
t
h
m
s
a
r
e
o
p
e
r
a
t
i
n
g
r
o
u
t
i
n
e
s
t
h
a
t
a
r
e
i
m
p
l
e
m
e
n
t
e
d
d
i
r
e
c
tl
y
o
n
t
h
e
r
o
b
o
t
c
o
n
t
r
o
l
l
e
r
a
n
d
d
e
p
e
n
d
o
n
t
h
e
r
e
s
p
o
n
s
e
o
f
t
h
e
s
e
n
s
o
r
s
t
o
t
h
e
e
n
v
i
r
o
n
m
e
n
t
[
1
2
-
1
4
]
.
I
n
s
u
m
m
ar
y
,
th
e
r
a
n
g
e
a
n
d
s
p
e
ed
o
f
r
esp
o
n
s
e
o
f
a
n
W
MR
d
ep
en
d
s
o
n
t
h
e
r
o
b
u
s
tn
ess
o
f
th
e
co
n
tr
o
ller
.
Ho
wev
er
,
th
e
ef
f
icien
cy
o
f
a
co
n
tr
o
ller
is
a
s
u
b
ject
u
n
d
er
s
tu
d
y
,
s
in
ce
,
cu
r
r
en
t
co
n
tr
o
l
ler
s
ar
e
s
u
b
ject
t
o
v
ar
i
ab
les
th
at
lim
it
th
e
am
o
u
n
t
o
f
m
o
v
e
m
en
t
o
f
th
e
ac
tu
at
o
r
s
an
d
th
e
a
b
ilit
y
to
e
x
p
lo
r
e
th
eir
en
v
ir
o
n
m
en
t.
T
h
er
ef
o
r
e,
th
is
ar
ticle
p
r
o
p
o
s
es
a
co
n
tr
o
l
s
tr
ateg
y
f
o
r
W
MR
th
at
g
en
er
ates
in
s
tr
u
ctio
n
lib
r
ar
ie
s
with
o
u
t
a
d
e
f
in
ed
d
ep
th
u
s
in
g
a
n
o
p
tim
izatio
n
alg
o
r
ith
m
s
o
th
at
th
e
r
o
b
o
t
p
er
f
o
r
m
s
m
o
v
em
en
ts
in
an
a
d
ap
tiv
e
way
.
T
h
e
alg
o
r
ith
m
d
ev
elo
p
e
d
an
d
th
e
r
esu
lts
o
b
tain
ed
ar
e
r
ep
o
r
ted
in
th
e
f
o
ll
o
win
g
s
ec
tio
n
s
,
w
h
ich
a
r
e
o
r
g
an
ized
as
f
o
llo
ws:
s
ec
tio
n
2
d
escr
ib
es
th
e
co
n
ce
p
ts
r
eq
u
ir
ed
to
u
n
d
e
r
s
tan
d
th
e
f
u
n
ctio
n
in
g
o
f
t
h
e
alg
o
r
ith
m
,
s
e
ctio
n
3
p
r
esen
ts
th
e
p
r
o
p
o
s
ed
alg
o
r
ith
m
an
d
d
escr
i
b
es its
o
p
er
atio
n
.
Fin
ally
,
s
ec
ti
o
n
4
p
r
esen
ts
th
e
r
esu
lts
o
b
tai
n
ed
.
2.
M
AT
E
R
I
AL
S
AND
M
E
T
H
O
DS
T
h
e
W
MR
co
n
tr
o
ller
p
r
o
p
o
s
ed
in
th
is
ar
ticle
co
d
es
th
e
m
o
v
e
m
en
ts
o
f
th
e
ac
tu
ato
r
s
o
f
a
u
n
i
cy
cle
r
o
b
o
t
u
s
in
g
th
e
R
OS
(
r
o
b
o
t
o
p
er
atin
g
s
y
s
te
m
)
lan
g
u
ag
e
,
f
r
o
m
th
e
m
o
tio
n
s
ets
b
u
ilt
u
s
in
g
an
o
p
tim
izatio
n
alg
o
r
ith
m
in
a
s
im
u
lato
r
s
et
u
p
f
o
r
th
at
p
u
r
p
o
s
e.
T
h
ese
co
n
ce
p
ts
ar
e
e
x
p
lain
ed
in
d
etail
in
th
is
s
ec
tio
n
.
2
.
1
.
U
nicy
cle
ro
bo
t
T
h
e
co
m
b
in
atio
n
o
f
d
if
f
er
e
n
t
ty
p
es
o
f
wh
ee
ls
o
r
th
eir
lo
ca
tio
n
with
in
th
e
s
tr
u
ctu
r
e
o
f
an
W
MR
ch
an
g
es
th
e
k
in
em
atics
o
f
th
e
r
o
b
o
t,
s
o
th
ey
h
a
v
e
b
ee
n
g
r
o
u
p
ed
in
d
if
f
e
r
en
t
way
s
.
Alth
o
u
g
h
t
h
e
f
u
n
ctio
n
ality
o
f
th
e
W
MR
allo
ws
it
to
b
e
clas
s
if
ied
ac
co
r
d
in
g
to
its
s
p
ec
ialty
(
e.
g
.
,
s
ea
r
ch
o
r
r
escu
e)
,
th
e
ar
r
an
g
em
e
n
t
o
f
its
wh
ee
ls
is
th
e
m
o
s
t
co
m
m
o
n
f
o
r
m
o
f
class
if
icatio
n
,
s
in
ce
it
d
if
f
er
en
tiates
r
o
b
o
ts
with
r
esp
ec
t
to
th
eir
m
an
eu
v
er
a
b
ilit
y
,
an
d
am
o
n
g
t
h
em
ar
e:
o
m
n
id
ir
ec
tio
n
al,
u
n
ic
y
cles,
tr
icy
cles a
n
d
q
u
a
d
r
icy
cl
es [
9
-
1
3
]
.
O
m
n
i
d
i
r
e
c
ti
o
n
a
l
r
o
b
o
t
s
h
a
v
e
a
g
r
e
a
t
e
r
n
u
m
b
e
r
o
f
d
e
g
r
e
e
s
o
f
f
r
e
e
d
o
m
(
n
u
m
b
e
r
o
f
a
x
es
i
n
w
h
i
ch
a
m
o
v
e
m
e
n
t
i
s
m
a
d
e
)
t
h
a
n
o
t
h
e
r
t
y
p
e
s
o
f
r
o
b
o
t
,
s
i
n
c
e
t
h
e
y
m
o
v
e
i
n
a
l
m
o
s
t
a
n
y
d
i
r
e
c
t
i
o
n
w
i
t
h
o
u
t
t
h
e
n
e
e
d
t
o
re
-
o
r
i
e
n
t
t
h
e
m
s
el
v
e
s
w
h
e
n
t
h
e
y
a
r
e
g
o
i
n
g
t
o
r
o
t
a
t
e
.
U
n
i
c
y
c
l
e
s
h
a
v
e
a
s
i
m
p
l
e
k
i
n
e
m
a
ti
cs
;
t
h
ei
r
s
t
r
u
c
t
u
r
e
is
c
o
m
p
o
s
e
d
o
f
t
w
o
f
i
x
e
d
w
h
e
e
ls
a
l
i
g
n
e
d
o
n
t
h
e
s
a
m
e
a
x
is
an
d
a
s
u
p
p
o
r
t
w
h
e
e
l
t
h
a
t
a
l
l
o
w
s
t
h
e
m
t
o
t
u
r
n
o
n
t
h
e
t
u
r
n
i
n
g
a
x
i
s
o
f
t
h
e
p
l
a
t
f
o
r
m
a
s
s
h
o
w
n
i
n
Fi
g
u
r
e
1
.
T
h
e
n
t
h
e
r
e
a
r
e
t
h
e
t
r
i
c
y
c
l
es
a
n
d
q
u
a
d
r
i
c
y
c
l
e
s
t
h
a
t
i
n
c
o
r
p
o
r
a
t
e
t
h
r
e
e
o
r
f
o
u
r
w
h
e
el
s
(
r
e
s
p
ec
t
i
v
el
y
)
i
n
t
h
ei
r
s
t
r
u
c
t
u
r
e
t
r
y
i
n
g
t
o
k
ee
p
t
h
e
c
e
n
t
e
r
o
f
g
r
a
v
it
y
i
n
t
h
e
ce
n
t
e
r
o
f
t
h
e
s
t
r
u
ct
u
r
e
w
h
e
n
i
n
c
r
e
a
s
i
n
g
t
h
e
r
o
b
o
t
s
p
e
ed
.
I
n
t
h
i
s
c
a
s
e
,
t
h
e
T
u
r
t
l
eB
o
t
u
n
i
c
y
c
l
e
r
o
b
o
t
w
a
s
u
s
e
d
,
w
h
i
c
h
c
o
n
s
i
s
t
s
o
f
a
m
o
d
u
l
a
r
c
h
a
s
s
i
s
t
o
p
l
ac
e
el
e
c
t
r
o
n
i
c
c
o
m
p
o
n
e
n
t
s
,
s
u
c
h
a
s
:
c
o
n
t
r
o
l
c
ar
d
s
,
c
a
m
e
r
as
,
s
e
n
s
o
r
s
o
r
a
cc
e
s
s
o
r
i
e
s
.
I
n
a
d
d
i
ti
o
n
,
t
h
e
c
o
n
t
r
o
l
l
e
r
o
f
t
h
i
s
r
o
b
o
t
i
n
t
e
r
p
r
e
t
s
R
OS
l
o
c
al
l
y
,
w
h
ic
h
a
l
l
o
w
s
t
h
e
u
s
e
r
t
o
u
s
e
a
s
t
a
n
d
a
r
d
la
n
g
u
a
g
e
f
o
r
m
t
o
c
o
n
t
r
o
l
t
h
e
r
o
b
o
t
m
o
v
e
m
e
n
t
s
[
1
5
]
.
On
th
e
o
n
e
h
an
d
,
th
e
r
o
b
o
tic
o
p
er
atin
g
s
y
s
tem
o
r
R
OS
is
a
s
tan
d
ar
d
ized
wo
r
k
en
v
ir
o
n
m
en
t
f
o
r
d
ev
elo
p
in
g
ap
p
licatio
n
s
f
o
r
r
o
b
o
ts
an
d
im
p
lem
en
ts
s
o
m
e
b
asic
s
er
v
ices
th
at
co
n
tem
p
la
te:
th
e
ab
s
tr
ac
tio
n
o
f
th
e
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e
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e
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o
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,
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m
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is
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etwe
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o
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o
ller
.
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h
is
en
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ir
o
n
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en
t
is
b
ased
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n
n
etwo
r
k
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wh
er
e
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e
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r
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s
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o
r
r
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en
d
s
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d
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u
ltip
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e
m
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m
th
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en
s
o
r
s
,
ac
tu
ato
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s
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d
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tates
o
f
th
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p
latf
o
r
m
.
Fu
r
th
e
r
m
o
r
e,
it
is
en
tire
ly
f
r
ee
an
d
ca
n
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e
im
p
lem
e
n
ted
in
a
UNI
X
o
p
er
atin
g
s
y
s
tem
(
e.
g
.
Ub
u
n
tu
)
[
1
6
,
1
7
]
.
O
n
t
h
e
o
t
h
e
r
h
a
n
d
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t
h
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[
x
,
y
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θ
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.
T
h
i
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m
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a
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A
l
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h
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q
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a
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d
o
d
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m
e
t
r
y
,
w
h
i
c
h
a
c
h
i
e
v
e
s
i
t
t
h
r
o
u
g
h
a
p
a
r
t
i
a
l
t
i
m
e
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
1
6
9
3
-
6
9
3
0
T
E
L
KOM
NI
KA
T
elec
o
m
m
u
n
C
o
m
p
u
t E
l Co
n
tr
o
l
,
Vo
l.
1
8
,
No
.
6
,
Dec
em
b
e
r
2
0
2
0
:
30
8
8
-
3
09
5
3090
i
n
t
e
g
r
a
t
i
o
n
o
f
m
o
v
e
m
e
n
t
s
[
1
5
-
1
7
]
.
O
n
e
o
f
t
h
e
a
d
v
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t
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g
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f
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F
i
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r
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2
(
b
)
[
1
8
]
.
(
a)
(
b
)
Fig
u
r
e
1
.
T
u
r
tleB
o
t m
o
b
ile
r
o
b
o
t
; (
a)
f
o
n
t v
iew,
a
n
d
(
b
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o
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iew
(
a)
(
b
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Fig
u
r
e
2
.
T
u
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tleB
o
t r
ef
er
en
ce
f
r
am
es
; (
a)
r
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e
r
en
ce
s
y
s
tem
,
an
d
(
b
)
v
ar
ia
b
lea
in
v
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l
v
ed
in
a
tu
r
n
Mo
r
e
s
p
ec
if
ically
,
to
d
eter
m
in
e
th
e
r
o
b
o
t
p
o
s
itio
n
,
m
ath
em
a
tical
tr
an
s
f
o
r
m
atio
n
s
b
ased
o
n
th
e
r
o
b
o
t
r
ef
er
en
ce
f
r
a
m
es
ar
e
u
s
ed
,
wh
er
e
th
e
p
ath
o
f
wh
ee
l
1
an
d
2
a
r
e
r
ep
r
esen
ted
b
y
(
1
)
,
th
e
f
o
r
m
o
f
th
e
av
er
ag
e
p
ath
by
(
2
)
an
d
th
e
o
r
ien
tatio
n
b
y
i
s
co
n
s
tr
u
cted
b
y
s
u
b
tr
ac
tin
g
t
wo
co
o
r
d
in
ates
an
d
tak
in
g
in
t
o
ac
co
u
n
t
th
e
wh
ee
l
s
p
ac
in
g
in
(
3
)
[
1
9
]
.
I
t
is
wo
r
th
n
o
tin
g
th
at
th
er
e
ar
e
o
th
er
way
s
to
d
eter
m
in
e
th
e
o
r
ien
tatio
n
o
f
a
r
o
b
o
t,
s
u
ch
as;
m
ea
s
u
r
em
en
t
with
a
GPS,
d
ig
i
tal
g
o
n
i
o
m
eter
s
o
r
g
y
r
o
s
co
p
es.
Ho
wev
er
,
th
e
s
c
h
em
e
im
p
le
m
en
ted
b
y
R
OS
f
o
r
th
e
h
an
d
lin
g
o
f
th
e
T
u
r
tleB
o
t
is
b
ased
o
n
th
e
o
d
o
-
m
etr
ic
tech
n
iq
u
e
an
d
th
u
s
d
eter
m
in
es
an
o
r
ien
tatio
n
an
d
r
elativ
e
p
o
s
itio
n
[
2
0
]
.
T
h
is
in
f
o
r
m
atio
n
is
r
elev
an
t
f
o
r
th
e
g
en
er
atio
n
o
f
th
e
c
o
n
tr
o
ller
u
s
in
g
an
o
p
tim
izatio
n
alg
o
r
ith
m
,
wh
ic
h
is
d
escr
ib
ed
in
th
e
f
o
llo
win
g
s
ec
tio
n
.
∆
=
∆
(
1
)
∆
=
∑
∆
2
1
2
(
2
)
∆
=
∑
∆
2
1
(
3
)
2
.
2
.
O
pti
m
i
z
a
t
io
n
a
lg
o
rit
hm
An
o
p
tim
izatio
n
alg
o
r
ith
m
is
a
tech
n
iq
u
e
th
at,
wh
en
im
p
le
m
en
ted
in
a
s
o
f
twar
e,
f
in
d
s
a
p
p
r
o
x
im
ate
v
alu
es
to
an
o
p
tim
al
v
alu
e.
Usu
ally
,
th
is
v
alu
e
ca
n
b
e
th
e
lar
g
est
o
r
th
e
s
m
allest
o
f
a
s
et
o
f
v
alu
es
an
d
to
f
in
d
it
th
e
alg
o
r
ith
m
p
er
f
o
r
m
s
a
s
er
ies
o
f
iter
atio
n
s
th
at
alter
th
e
in
p
u
t
v
alu
es
ac
c
o
r
d
in
g
to
a
n
a
p
titu
d
e
v
alu
e.
W
h
er
e
,
th
e
p
r
o
f
icien
c
y
v
alu
e
r
e
p
r
es
en
ts
th
e
p
er
f
o
r
m
an
ce
o
f
t
h
e
v
ar
iab
le
wh
en
m
o
d
if
y
i
n
g
t
h
e
in
p
u
t
v
a
r
iab
les
(
in
d
iv
id
u
al
)
o
f
t
h
e
f
u
n
ctio
n
[
2
1
,
22
].
a)
R
ef
er
en
ce
s
y
s
tem
b)
Var
iab
les
in
v
o
lv
ed
i
n
a
tu
r
n
Evaluation Warning : The document was created with Spire.PDF for Python.
T
E
L
KOM
NI
KA
T
elec
o
m
m
u
n
C
o
m
p
u
t E
l Co
n
tr
o
l
Desig
n
o
f a
co
n
tr
o
ller
fo
r
w
h
e
eled
mo
b
ile
r
o
b
o
ts
b
a
s
ed
o
n
a
u
to
ma
tic
mo
ve
men
t
(
Ho
lma
n
Mo
n
tiel
A
r
iz
a
)
3091
A
ca
s
e
o
f
o
p
tim
izatio
n
alg
o
r
ith
m
s
is
g
en
etic
alg
o
r
ith
m
s
(
GA
)
,
wh
ich
iter
ativ
ely
u
p
d
ates a
p
o
p
u
latio
n
o
f
in
d
iv
id
u
als
to
s
elec
t
th
e
b
e
s
t
am
o
n
g
th
em
.
I
n
a
s
im
ilar
w
ay
to
n
atu
r
al
s
elec
tio
n
,
GA
i
m
p
lem
en
t
s
elec
tio
n
,
cr
o
s
s
in
g
an
d
m
u
tatio
n
m
eth
o
d
s
,
wh
ich
f
r
o
m
a
n
ap
titu
d
e
v
alu
e
ass
ig
n
ed
to
ev
er
y
o
n
e
d
e
ter
m
in
e
wh
eth
er
it
s
u
r
v
iv
es
wh
en
u
p
d
atin
g
th
e
p
o
p
u
latio
n
(
g
e
n
er
atio
n
al
c
h
an
g
e)
[
2
3
].
G
A
s
ar
e
v
er
y
v
e
r
s
atile
to
o
ls
an
d
ca
n
b
e
ad
ap
ted
t
o
alm
o
s
t
a
n
y
n
ee
d
,
s
o
th
er
e
ar
e
m
an
y
v
ar
ieties
an
d
class
es.
I
n
its
s
im
p
lest
f
o
r
m
is
th
e
co
n
v
en
tio
n
al
GA
th
at
h
as
n
o
r
estrictio
n
s
o
n
th
e
ap
p
licatio
n
o
f
s
elec
tio
n
,
c
r
o
s
s
in
g
an
d
m
u
tatio
n
o
p
er
atio
n
s
,
an
d
a
v
a
r
ian
t
o
f
its
co
m
p
u
tatio
n
al
im
p
lem
en
tat
io
n
co
n
v
er
ts
in
d
iv
id
u
als in
to
b
in
ar
y
n
u
m
b
er
s
to
f
ac
ilit
ate
cr
o
s
s
in
g
an
d
m
u
tatio
n
o
p
er
atio
n
s
(
Alg
o
r
ith
m
1
)
[
2
4
-
2
7
].
Algorithm 1. Conventional genetic algorithm
i=0
P0=0
While
i
<
c
o
n
dit
i
o
n
of
t
e
r
m
i
n
a
t
i
o
n
do
←
ℎ
(
0
)
1
←
(
0
,
)
1
←
(
1
,
)
1
←
(
1
)
+
+
Selectio
n
o
p
er
ato
r
s
r
an
k
in
d
i
v
id
u
als
b
ased
o
n
t
h
eir
s
k
ill
v
alu
e,
in
clu
d
in
g
r
o
u
lette
an
d
t
o
u
r
n
am
e
n
t
m
eth
o
d
s
.
First,
th
e
r
o
u
lette
m
eth
o
d
ass
ig
n
s
ev
er
y
o
n
e
a
p
r
o
b
ab
ilit
y
v
alu
e
b
ased
o
n
h
is
o
r
h
er
s
k
ill
v
alu
e,
i.e
.
a
p
r
o
b
ab
ilit
y
d
is
tr
ib
u
tio
n
is
co
n
s
tr
u
cted
b
y
ad
d
in
g
u
p
all
th
e
s
k
ill
v
alu
es
o
f
th
e
in
d
iv
id
u
als
an
d
d
iv
id
in
g
th
em
b
y
th
at
r
esu
lt.
T
h
en
th
e
m
eth
o
d
g
en
er
ates
an
d
s
ea
r
ch
es
f
o
r
th
e
in
d
iv
id
u
al
with
th
e
clo
s
est
p
r
o
b
ab
ilit
y
,
to
th
at
o
f
a
r
an
d
o
m
ly
g
e
n
er
ated
n
u
m
b
e
r
.
Seco
n
d
l
y
,
th
e
t
o
u
r
n
a
m
en
t
m
eth
o
d
s
elec
ts
f
o
u
r
in
d
i
v
id
u
als
at
r
an
d
o
m
an
d
co
m
p
ar
es th
eir
s
k
ill v
alu
e
,
am
o
n
g
th
e
m
it selects two
an
d
f
r
o
m
th
e
two
r
esu
ltan
ts
it selects o
n
e.
T
h
e
b
in
ar
y
cr
o
s
s
in
g
o
p
e
r
ato
r
tak
es
two
in
d
iv
id
u
als
f
r
o
m
th
e
p
o
p
u
latio
n
,
co
d
es
th
em
in
t
o
a
b
in
ar
y
n
u
m
b
er
an
d
cu
ts
th
eir
r
esu
ltin
g
b
in
a
r
y
s
tr
in
g
s
at
a
r
an
d
o
m
p
o
in
t
an
d
r
ec
o
m
b
i
n
es
th
em
t
o
g
en
er
ate
two
n
ew
in
d
iv
id
u
als
,
s
ee
Fig
u
r
e
3
(
a)
.
Similar
ly
,
th
e
m
u
tatio
n
o
p
e
r
ato
r
s
elec
ts
an
in
d
iv
id
u
al,
co
n
v
er
ts
it
in
to
a
b
in
ar
y
n
u
m
b
er
an
d
ch
a
n
g
es
th
e
v
alu
e
o
f
a
b
it
,
s
ee
Fig
u
r
e
3
(
b
)
.
T
h
e
p
r
o
p
o
s
ed
co
n
tr
o
ller
b
u
ild
s
m
o
tio
n
s
ets
u
s
in
g
a
m
o
d
i
f
ied
G
A,
in
w
h
ich
th
e
n
u
m
b
er
o
f
cr
o
s
s
in
g
o
p
e
r
ato
r
s
is
in
cr
ea
s
ed
an
d
to
g
eth
er
with
th
e
m
u
tatio
n
o
p
er
ato
r
th
ey
ar
e
s
elec
ted
with
a
p
r
ed
ef
in
ed
th
r
esh
o
ld
v
al
u
e.
T
h
ese
v
a
lu
es
an
d
th
e
im
p
lem
en
tatio
n
o
f
th
e
m
o
d
if
ied
G
A
ar
e
d
escr
ib
ed
in
d
etail
in
th
e
f
o
llo
win
g
s
ec
tio
n
.
(
a)
(
b
)
Fig
u
r
e
3
.
Gen
etic
o
p
er
ato
r
s
; (
a)
cr
o
s
s
in
g
o
p
e
r
ato
r
,
a
n
d
(
b
)
m
u
tatio
n
o
p
e
r
ato
r
3.
DE
V
E
L
O
P
M
E
N
T
AND
I
M
P
L
E
M
E
NT
A
T
I
O
N
I
n
itially
,
th
e
co
n
tr
o
ller
g
en
e
r
a
tes
a
s
e
t
o
f
20
in
d
iv
id
u
als
,
wh
ich
ar
e
s
ets
o
f
p
ar
am
eter
s
co
d
ed
in
R
O
S
lan
g
u
ag
e
th
at
allo
w
t
h
e
r
o
b
o
t
to
ex
ec
u
te
m
o
v
em
en
t
r
o
u
tin
e
s
,
wh
er
e,
th
e
s
u
itab
ilit
y
v
al
u
e
is
th
e
d
is
tan
ce
t
h
e
r
o
b
o
t
tr
av
els
with
th
e
i
n
d
icati
o
n
s
g
iv
e
n
b
y
th
e
i
n
d
iv
id
u
al
d
u
r
in
g
1
0
s
ec
o
n
d
s
,
s
ee
Fig
u
r
e
4
(
a)
.
E
ac
h
s
et
o
f
p
ar
am
eter
s
is
co
m
p
o
s
ed
b
y
g
r
o
u
p
s
o
f
u
p
to
5
co
m
p
o
n
e
n
ts
(
g
en
er
ate
d
at
r
an
d
o
m
)
,
th
at
ca
n
b
e
f
o
r
war
d
m
o
v
em
en
ts
(
d
u
r
atio
n
b
etwe
e
n
0
an
d
5
s
)
,
b
ac
k
war
d
m
o
v
e
m
en
ts
(
d
u
r
atio
n
b
etwe
en
1
a
n
d
5
s
)
a
n
d
tu
r
n
in
g
(
b
e
tw
e
e
n
−
π
a
n
d
π
)
,
s
ee
Fig
u
r
e
4
(
b
)
.
L
ik
e
th
e
c
o
n
v
e
n
tio
n
al
GA,
t
h
e
s
elec
tio
n
m
eth
o
d
in
co
r
p
o
r
ated
in
th
is
v
er
s
io
n
o
f
th
e
GA
is
to
u
r
n
am
e
n
t
an
d
th
e
s
elec
tio
n
in
ea
ch
p
h
ase
is
m
ad
e
f
o
llo
win
g
a
B
er
n
u
lli
d
is
tr
ib
u
tio
n
,
s
h
o
wn
in
Fig
u
r
e
5
.
T
h
at
is
,
ev
er
y
o
n
e
h
as
a
f
if
ty
p
er
ce
n
t
(
5
0
%)
ch
an
ce
o
f
a
d
v
an
cin
g
to
th
e
n
ex
t
p
h
ase.
I
n
ad
d
itio
n
,
t
h
is
m
eth
o
d
i
s
co
m
p
lem
en
ted
b
y
th
e
s
elec
tio
n
an
d
cr
o
s
s
in
g
m
e
th
o
d
s
,
s
in
ce
th
r
esh
o
ld
s
wer
e
s
et
f
o
r
th
e
alg
o
r
ith
m
to
s
elec
t
am
o
n
g
th
e
g
en
etic
o
p
er
ato
r
s
o
f
cr
o
s
s
in
g
a
n
d
m
u
tatio
n
.
On
th
e
o
n
e
h
a
n
d
,
th
e
r
e
ar
e
th
r
ee
o
p
tio
n
s
f
o
r
s
elec
tin
g
a
g
en
e
tic
cr
o
s
s
o
p
er
ato
r
.
T
h
e
f
ir
s
t
o
p
t
io
n
s
h
o
wn
in
Fig
u
r
e
6
(
a)
,
th
e
co
m
b
in
atio
n
o
f
ch
ar
ac
ter
is
tics
b
etwe
en
two
r
an
d
o
m
ly
s
elec
ted
in
d
i
v
id
u
als.
T
h
e
s
ec
o
n
d
s
h
o
wn
i
n
Fig
u
r
e
6
(
b
)
,
th
e
co
m
b
in
atio
n
o
f
s
im
ilar
p
ar
a
m
eter
s
(
if
an
y
)
b
etwe
en
in
d
i
v
id
u
als,
e.
g
.
if
b
o
th
in
d
iv
id
u
als
h
av
e
th
e
tu
r
n
attr
i
b
u
te
th
eir
tu
r
n
an
g
les
ar
e
ex
c
h
an
g
ed
.
T
h
e
th
i
r
d
s
h
o
wn
in
F
ig
u
r
e
6
(
c)
,
o
p
er
ato
r
s
im
p
ly
r
ev
er
s
es
th
e
c
o
m
p
o
n
en
ts
o
f
a
s
elec
ted
in
d
iv
id
u
al.
On
th
e
o
th
e
r
h
a
n
d
,
th
e
g
e
n
e
tic
m
u
tatio
n
o
p
er
ato
r
is
s
im
p
ler
,
s
ee
Fig
u
r
e
6
(
d
)
,
s
in
c
e
it c
h
an
g
es a
n
in
d
iv
id
u
al
i
n
th
e
p
o
p
u
latio
n
f
o
r
a
n
ew
o
n
e.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
1
6
9
3
-
6
9
3
0
T
E
L
KOM
NI
KA
T
elec
o
m
m
u
n
C
o
m
p
u
t E
l Co
n
tr
o
l
,
Vo
l.
1
8
,
No
.
6
,
Dec
em
b
e
r
2
0
2
0
:
30
8
8
-
3
09
5
3092
(
a)
(
b
)
Fig
u
r
e
4
.
Gen
e
r
al
d
iag
r
a
m
o
f
an
in
d
iv
id
u
al
; (
a)
c
o
m
p
o
s
itio
n
o
f
an
i
n
d
iv
id
u
al
an
d
t
h
e
p
o
p
u
l
atio
n
,
an
d
(
b
)
v
iew
o
f
th
e
p
ar
am
eter
s
to
b
e
ev
alu
ated
a
n
d
ca
lcu
latio
n
o
f
th
e
s
u
itab
ilit
y
v
alu
e
(
a)
(
b
)
Fig
u
r
e
5
.
Fu
n
ctio
n
al
d
iag
r
am
s
o
f
an
i
n
d
iv
id
u
al'
s
as
s
es
s
m
en
t m
eth
o
d
a
n
d
s
elec
tio
n
m
eth
o
d
;
(
a)
in
d
iv
i
d
u
al
ass
ess
m
en
t sch
em
e,
an
d
(
b
)
r
ep
r
esen
tatio
n
o
f
t
h
e
s
elec
tio
n
m
eth
o
d
(
a)
(
b
)
(
c)
(
d
)
Fig
u
r
e
6
.
Gen
etic
c
r
o
s
s
an
d
m
u
tatio
n
o
p
e
r
ato
r
s
; (
a)
g
en
etic
c
r
o
s
s
in
g
o
p
er
ato
r
1
,
(
b
)
g
e
n
etic
cr
o
s
s
in
g
o
p
er
ato
r
2
,
(
c)
g
e
n
etic
cr
o
s
s
in
g
o
p
er
ato
r
3
,
an
d
(
d
)
g
en
etic
cr
o
s
s
in
g
o
p
e
r
ato
r
4
Gen
etic
cr
o
s
s
in
g
an
d
m
u
tatio
n
o
p
e
r
ato
r
s
ar
e
co
m
b
i
n
ed
wit
h
a
co
n
v
en
tio
n
al
GA,
wh
ich
i
n
co
r
p
o
r
ates
a
f
ix
ed
th
r
esh
o
ld
f
o
r
th
e
s
elec
tio
n
o
f
ea
ch
in
d
iv
id
u
al
o
p
er
at
o
r
.
T
h
at
is
,
in
ea
ch
iter
atio
n
o
f
th
e
m
o
d
if
ied
GA
th
e
ex
ec
u
tio
n
o
f
an
o
p
er
at
o
r
d
ep
en
d
s
o
n
a
r
an
d
o
m
n
u
m
b
e
r
th
at
f
o
llo
ws
a
u
n
if
o
r
m
d
is
tr
i
b
u
tio
n
.
I
t
s
h
o
u
l
d
b
e
n
o
ted
th
at
wh
e
n
an
in
d
iv
id
u
al
is
ev
alu
ated
,
th
e
ex
ec
u
tio
n
o
f
th
e
m
o
d
if
ied
GA
is
s
to
p
p
ed
u
n
til
th
e
s
im
u
lato
r
r
esp
o
n
d
s
with
a
v
al
u
e
f
o
r
th
e
d
is
tan
ce
tr
av
elled
(
Alg
o
r
ith
m
2
)
.
Algorithm 2. Modified genetic algorithm
Function
Genetic algorithm ()
P0[20][5] =initial population;
Generations=0;
P0←Generate 20 individuals at random;
While
Generations<Termination condition
do
d←Evaluate (P0)
P1←Tournament (P0, d)
Evaluation Warning : The document was created with Spire.PDF for Python.
T
E
L
KOM
NI
KA
T
elec
o
m
m
u
n
C
o
m
p
u
t E
l Co
n
tr
o
l
Desig
n
o
f a
co
n
tr
o
ller
fo
r
w
h
e
eled
mo
b
ile
r
o
b
o
ts
b
a
s
ed
o
n
a
u
to
ma
tic
mo
ve
men
t
(
Ho
lma
n
Mo
n
tiel
A
r
iz
a
)
3093
S~U [0,1]
If
S< 0.25
then
P1←Crossing operator 1(P1)
Else If
S>=0.25 and S<0. 5
then
P1←Crossing operator 2 (P1)
Else If
S>=
0.5 and S<0. 75
then
P1←Crossing operator 3 (P1)
Else
P1←Mutation Operator (P1)
P0←P1
Generations++
Function
Evaluation (P)
T←0
Establishing a connection with the virtual machine
If
T<20
then
Encode Individual with ROS tags (P0[T] [1:5])
p_1=read current position ()
Send motion parameters to the simulator (P0[T] [1:5])
Wait 10 seconds
p_2=read curre
nt position ()
d[T]=|p_1
-
p_2 |
T++
Return
d
I
n
o
r
d
er
to
ev
al
u
ate
th
e
p
er
f
o
r
m
an
ce
o
f
th
e
m
o
d
if
ie
d
GA,
an
im
p
lem
en
tatio
n
o
f
th
e
co
n
v
en
tio
n
al
GA
was
ca
r
r
ied
o
u
t.
T
h
is
im
p
le
m
en
tatio
n
was
d
o
n
e
ac
co
r
d
in
g
to
Alg
o
r
ith
m
1
,
wh
e
r
e
th
e
s
elec
tio
n
m
eth
o
d
is
r
o
u
lette
an
d
th
e
cr
o
s
s
an
d
m
u
t
atio
n
o
p
e
r
ato
r
is
p
r
esen
ted
i
n
Fig
u
r
e
6
(
a
)
a
n
d
Fig
u
r
e
6
(
d
)
r
esp
ec
tiv
ely
.
I
n
b
o
t
h
ca
s
es,
th
e
test
en
v
ir
o
n
m
e
n
t
is
t
h
e
wo
r
k
in
g
s
p
ac
e
o
f
th
e
g
az
eb
o
s
im
u
lato
r
an
d
th
e
m
ain
o
b
jectiv
e
o
f
th
e
T
u
r
tleB
o
t
is
to
tr
av
el
th
e
g
r
ea
test
d
is
tan
ce
p
o
s
s
ib
le,
in
a
f
lat
e
n
v
ir
o
n
m
e
n
t
with
an
d
with
o
u
t
o
b
s
tacle
s
,
s
ee
Fig
u
r
e
7
.
T
h
is
wo
r
k
s
p
ac
e
is
in
s
talled
in
a
v
i
r
tu
al
m
ac
h
in
e
with
L
I
NUX
o
p
er
atin
g
s
y
s
tem
,
wh
ich
r
an
o
n
a
co
m
p
u
ter
wit
h
W
I
NDO
W
S
1
0
o
p
e
r
atin
g
s
y
s
t
em
,
an
I
n
tel
i
n
s
id
e
T
M
c
o
r
e
i3
p
r
o
ce
s
s
o
r
,
8
Gb
s
o
f
R
AM
m
e
m
o
r
y
an
d
a
2
4
0
Gb
s
h
ar
d
d
is
k
.
(
a)
(
b
)
Fig
u
r
e
7.
T
est s
ce
n
ar
io
s
; (
a)
u
n
o
b
s
tr
u
cted
e
n
v
ir
o
n
m
en
t,
a
n
d
(
b
)
o
b
s
tacle
en
v
ir
o
n
m
en
t
4.
RE
SU
L
T
S AN
D
AN
AL
Y
SI
S
T
h
e
A
G
s
m
e
n
t
i
o
n
e
d
i
n
s
e
c
t
i
o
n
2
a
n
d
s
e
c
t
i
o
n
3
c
a
r
r
i
e
d
o
u
t
1
0
0
0
a
p
t
i
t
u
d
e
v
a
l
u
e
e
v
a
l
u
a
t
i
o
n
s
o
v
e
r
5
0
a
l
g
o
r
i
t
h
m
r
u
n
s
.
T
h
a
t
i
s
,
e
a
c
h
a
l
g
o
r
i
t
h
m
h
a
d
a
g
e
n
e
r
a
t
i
o
n
a
l
c
h
a
n
g
e
o
v
e
r
5
0
g
e
n
e
r
a
t
i
o
n
s
i
n
b
o
t
h
s
c
e
n
a
r
i
o
s
.
I
n
t
h
e
u
n
o
b
s
t
r
u
c
t
e
d
e
n
v
i
r
o
n
m
e
n
t
,
t
h
e
i
n
f
o
r
m
a
t
i
o
n
c
o
l
l
e
c
t
e
d
a
l
l
o
w
e
d
t
h
e
c
o
n
s
t
r
u
c
t
i
o
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o
f
t
h
e
g
r
a
p
h
s
s
h
o
w
n
i
n
F
i
g
u
r
e
8
t
h
a
t
s
h
o
w
t
h
e
s
t
a
n
d
a
r
d
d
e
v
i
a
t
i
o
n
,
t
h
e
a
v
e
r
a
g
e
,
t
h
e
b
e
s
t
a
n
d
t
h
e
w
o
r
s
t
w
i
t
h
r
e
s
p
e
c
t
t
o
t
h
e
d
i
s
t
a
n
c
e
t
r
a
v
e
l
e
d
.
I
n
t
h
e
e
n
v
i
r
o
n
m
e
n
t
w
i
t
h
o
b
s
t
a
c
l
e
s
,
t
h
e
i
n
f
o
r
m
a
t
i
o
n
c
o
l
l
e
c
t
e
d
a
l
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o
w
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d
t
h
e
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o
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t
r
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c
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o
n
o
f
t
h
e
g
r
a
p
h
s
i
n
F
i
g
u
r
e
9
,
w
h
e
r
e
t
h
e
b
e
h
a
v
i
o
r
o
f
t
h
e
r
o
b
o
t
w
h
e
n
t
r
y
i
n
g
t
o
a
v
o
i
d
o
b
s
t
a
c
l
e
s
a
n
d
t
r
a
v
e
l
t
h
r
o
u
g
h
i
t
s
e
n
v
i
r
o
n
m
e
n
t
d
u
r
i
n
g
a
c
o
n
v
e
n
t
i
o
n
a
l
a
n
d
m
o
d
i
f
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e
d
GA
e
x
e
c
u
t
i
o
n
r
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s
p
e
c
t
i
v
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y
i
s
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r
e
s
e
n
t
e
d
.
C
o
n
s
i
d
e
r
i
n
g
t
h
a
t
5
0
G
A
r
u
n
s
w
e
r
e
p
e
r
f
o
r
m
e
d
i
n
t
h
e
o
b
s
t
a
c
l
e
e
n
v
i
r
o
n
m
e
n
t
,
t
h
e
b
e
s
t
s
u
i
t
a
b
i
l
i
t
y
v
a
l
u
e
f
o
u
n
d
b
y
e
a
c
h
a
l
g
o
r
i
t
h
m
i
s
p
r
e
s
e
n
t
e
d
i
n
T
a
b
l
e
1
.
F
i
n
a
l
l
y
,
t
h
e
i
n
t
e
r
p
r
e
t
a
t
i
o
n
o
f
t
h
e
r
e
s
u
l
t
s
o
b
t
a
i
n
e
d
i
s
p
r
e
s
e
n
t
e
d
i
n
t
h
e
f
o
l
l
o
w
i
n
g
s
e
c
t
i
o
n
.
T
ab
le
1
.
B
est f
it v
alu
e
f
o
u
n
d
in
th
e
o
b
s
tacle
en
v
ir
o
n
m
en
t b
y
b
o
th
GA
C
o
n
v
e
n
t
i
o
n
a
l
G
A
M
o
d
i
f
i
e
d
G
A
D
i
st
a
n
c
e
t
r
a
v
e
l
l
e
d
[
c
m]
43
.
12
±
5
.
76
35
.
15
±
6
.
88
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
1
6
9
3
-
6
9
3
0
T
E
L
KOM
NI
KA
T
elec
o
m
m
u
n
C
o
m
p
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t E
l Co
n
tr
o
l
,
Vo
l.
1
8
,
No
.
6
,
Dec
em
b
e
r
2
0
2
0
:
30
8
8
-
3
09
5
3094
(
a)
(
b
)
Fig
u
r
e
8.
T
r
en
d
o
f
co
n
v
en
ti
o
n
al
an
d
m
o
d
if
ied
GA
in
a
n
u
n
h
i
n
d
er
ed
e
n
v
ir
o
n
m
en
t
;
(
a)
co
n
v
en
tio
n
al
GA
(
X
5
0
)
,
a
n
d
(
b
)
m
o
d
if
ied
GA
(
X5
0
)
(
a)
(
b
)
Fig
u
r
e
9.
T
r
en
d
o
f
co
n
v
en
ti
o
n
al
an
d
m
o
d
if
ied
GA
in
a
h
in
d
e
r
ed
en
v
i
r
o
n
m
e
n
t
;
(
a)
co
n
v
en
tio
n
al
GA,
a
n
d
(
b
)
m
o
d
if
ied
GA
5.
CO
NCLU
SI
O
N
S
T
h
e
co
n
v
en
tio
n
al
GA
is
v
er
s
at
ile
en
o
u
g
h
to
a
d
ap
t
to
t
h
e
n
ee
d
s
o
f
ea
ch
s
itu
atio
n
,
w
h
ich
i
n
d
icate
s
th
at
th
is
ty
p
e
o
f
o
p
tim
izatio
n
alg
o
r
i
th
m
s
is
n
o
t
ex
clu
s
iv
ely
u
s
ed
f
o
r
th
e
s
o
lu
tio
n
o
f
co
m
p
lex
m
at
h
em
atica
l
p
r
o
b
lem
s
.
As
s
h
o
wn
in
Fig
u
r
e
4
(
b
)
th
e
f
u
n
ctio
n
to
b
e
o
p
tim
ized
h
as
o
n
ly
o
n
e
a
r
ith
m
etic
o
p
er
ato
r
a
n
d
d
ep
e
n
d
s
o
n
two
v
alu
es
to
f
ix
th
e
ap
titu
d
e
v
al
u
e
o
f
an
in
d
iv
i
d
u
al,
w
h
ich
in
d
icate
s
th
at
d
eter
m
in
i
n
g
a
n
a
p
titu
d
e
v
al
u
e
ca
n
b
e
ac
h
iev
ed
f
r
o
m
s
im
p
le
m
ath
e
m
atica
l
tr
an
s
f
o
r
m
atio
n
s
.
I
n
ad
d
itio
n
,
th
is
ty
p
e
o
f
tr
a
n
s
f
o
r
m
atio
n
r
eq
u
ir
es
a
l
o
w
co
m
p
u
tatio
n
al
c
o
s
t,
wh
ich
g
iv
es th
e
u
s
er
th
e
f
lex
ib
ilit
y
to
im
p
lem
en
t it
in
a
co
n
v
en
tio
n
al
d
esk
to
p
co
m
p
u
ter
.
T
h
e
g
r
ap
h
s
in
Fig
u
r
e
8
s
h
o
w
t
h
at
b
o
th
G
A
s
f
in
d
in
d
iv
id
u
als
th
at
allo
w
th
e
r
o
b
o
t
to
tr
av
el
u
p
to
5
0
cm
in
a
s
tr
aig
h
t
lin
e,
wh
ich
in
d
icate
s
th
at
d
u
r
in
g
th
e
ev
o
lu
tio
n
th
e
tu
r
n
in
g
an
d
b
ac
k
war
d
m
o
v
em
en
ts
d
is
ap
p
ea
r
.
Alth
o
u
g
h
,
b
o
th
GA
f
in
d
r
elati
v
ely
g
o
o
d
co
n
f
ig
u
r
atio
n
s
o
f
i
n
d
iv
id
u
als,
in
co
n
v
en
tio
n
al
GA
it
h
as
a
l
o
wer
s
p
ee
d
o
f
co
n
v
er
g
e
n
ce
,
s
in
ce
,
t
h
e
m
u
tatio
n
o
p
e
r
ato
r
m
ak
es
v
er
y
s
tr
o
n
g
ch
a
n
g
es
to
th
e
p
o
p
u
la
tio
n
in
cr
ea
s
in
g
i
n
co
n
v
er
g
en
ce
tim
e.
Ho
wev
e
r
,
b
y
lim
itin
g
th
e
ef
f
ec
t o
f
th
e
m
u
tatio
n
o
p
er
at
o
r
o
n
th
e
m
o
d
if
i
ed
GA,
th
e
ef
f
ec
t
o
f
th
e
m
u
tatio
n
o
p
e
r
ato
r
is
r
ed
u
c
ed
an
d
s
o
is
th
e
co
n
v
er
g
e
n
ce
ti
m
e.
I
n
ad
d
itio
n
,
in
c
r
ea
s
in
g
th
e
n
u
m
b
er
o
f
cr
o
s
s
in
g
o
p
er
ato
r
s
tak
es a
d
v
an
tag
e
o
f
t
h
e
ch
ar
ac
ter
is
tics
o
f
g
o
o
d
in
d
i
v
id
u
als in
th
e
p
o
p
u
latio
n
.
T
h
e
g
r
ap
h
s
in
Fig
ur
e
9
wer
e
n
o
t m
ad
e
in
a
s
im
ilar
way
to
th
e
u
n
o
b
s
tr
u
cted
e
n
v
ir
o
n
m
en
t,
a
s
th
e
r
o
b
o
t
m
ak
es
r
an
d
o
m
m
o
v
em
e
n
ts
wh
en
it
en
co
u
n
ter
s
an
o
b
s
tacle
,
wh
ich
d
o
es
n
o
t
allo
w
a
co
n
v
er
g
e
n
ce
o
f
th
e
p
r
o
f
icien
c
y
v
alu
e.
As
s
h
o
wn
in
th
e
lo
wer
p
ar
t
o
f
th
e
g
r
a
p
h
,
s
o
m
etim
es
th
e
p
r
o
f
icien
c
y
v
alu
e
is
less
th
an
1
0
c
m
wh
ich
m
ea
n
s
th
at
th
e
r
o
b
o
t
is
t
r
ap
p
e
d
o
r
ca
n
n
o
t
av
o
i
d
th
e
o
b
s
tacle
f
o
r
a
ce
r
tain
ti
m
e.
Ho
wev
er
,
wh
e
n
d
o
d
g
in
g
th
e
o
b
s
tacle
,
th
e
ap
titu
d
e
v
alu
e
in
cr
ea
s
es
r
ap
id
ly
,
w
h
ich
lead
s
b
o
th
G
A
s
to
f
o
llo
w
th
at
p
ath
an
d
th
u
s
t
h
e
r
o
b
o
t
tr
av
els
th
r
o
u
g
h
m
u
c
h
o
f
th
e
en
v
ir
o
n
m
en
t.
Alth
o
u
g
h
,
t
h
e
s
am
e
ch
ar
ac
ter
is
tics
o
f
Fig
u
r
e
8
wer
e
n
o
t
m
ea
s
u
r
ed
,
T
ab
le
1
was c
o
n
s
tr
u
cted
s
h
o
win
g
th
e
b
est in
d
iv
id
u
al
f
o
u
n
d
b
y
b
o
th
GA,
o
f
wh
i
ch
th
e
m
o
d
if
ie
d
GA
f
o
u
n
d
s
o
m
e
p
a
r
tially
b
etter
o
n
es.
Evaluation Warning : The document was created with Spire.PDF for Python.
T
E
L
KOM
NI
KA
T
elec
o
m
m
u
n
C
o
m
p
u
t E
l Co
n
tr
o
l
Desig
n
o
f a
co
n
tr
o
ller
fo
r
w
h
e
eled
mo
b
ile
r
o
b
o
ts
b
a
s
ed
o
n
a
u
to
ma
tic
mo
ve
men
t
(
Ho
lma
n
Mo
n
tiel
A
r
iz
a
)
3095
RE
F
E
R
E
NC
E
S
[1
]
T
.
T
o
k
u
n
a
g
a
,
K
.
O
k
a
a
n
d
A
.
H
a
r
a
d
a
,
"
1
s
e
g
m
e
n
t
c
o
n
t
i
n
u
u
m
m
a
n
i
p
u
l
a
t
o
r
f
o
r
a
u
t
o
m
a
t
i
c
h
a
r
v
e
s
t
i
n
g
r
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b
o
t
-
p
r
o
t
o
t
y
p
e
a
n
d
m
o
d
e
l
i
n
g
,
"
2
0
1
7
I
E
E
E
I
n
t
e
r
n
a
t
i
o
n
a
l
C
o
n
f
e
r
e
n
c
e
o
n
M
e
c
h
a
t
r
o
n
i
c
s
a
n
d
A
u
t
o
m
a
t
i
o
n
(
I
C
M
A
)
,
p
p
.
1
6
5
5
-
1659
,
2
0
1
7
.
[2
]
Y.
Wan
a
n
d
H.
X
u
,
"
On
d
y
n
a
m
ics
sim
u
latio
n
o
f
3
DO
F
m
a
n
ip
u
lato
r
,
"
2
0
1
6
3
5
t
h
C
h
in
e
se
Co
n
tro
l
Co
n
fer
e
n
c
e
(CCC)
,
p
p
.
6
2
9
0
-
6
2
9
4
,
2
0
1
6
.
[3
]
S
.
Ku
c
u
k
a
n
d
B.
D.
G
u
n
g
o
r
,
"
I
n
v
e
rse
k
in
e
m
a
ti
c
s
so
lu
ti
o
n
o
f
a
n
e
w
h
y
b
rid
ro
b
o
t
m
a
n
i
p
u
lato
r
p
ro
p
o
s
e
d
fo
r
m
e
d
ica
l
p
u
r
p
o
se
s,"
2
0
1
6
M
e
d
ic
a
l
T
e
c
h
n
o
l
o
g
ies
Na
ti
o
n
a
l
Co
n
g
re
ss
(T
IPT
E
KNO)
,
p
p
.
1
-
4
,
2
0
1
6
.
[4
]
J.
M
e
n
g
,
A.
Li
u
,
Y.
Ya
n
g
,
Z.
W
u
a
n
d
Q.
X
u
,
"
Two
-
Wh
e
e
led
Ro
b
o
t
P
latf
o
rm
Ba
se
d
o
n
P
ID
Co
n
t
ro
l,
"
2
0
1
8
5
t
h
In
ter
n
a
t
io
n
a
l
C
o
n
fer
e
n
c
e
o
n
I
n
fo
r
ma
ti
o
n
S
c
ien
c
e
a
n
d
Co
n
tro
l
En
g
i
n
e
e
rin
g
(ICI
S
CE)
,
p
p
.
1
0
1
1
-
1
0
1
4
,
2
0
1
8
.
[5
]
H.
Wata
n
a
b
e
,
"
De
v
e
lo
p
m
e
n
t
o
f
Wafe
r
Tran
sfe
r
S
imu
lat
o
r
Ba
se
d
o
n
Ce
ll
u
lar
Au
t
o
m
a
ta,"
IEE
E
T
ra
n
sa
c
ti
o
n
s
o
n
S
e
mic
o
n
d
u
c
to
r
M
a
n
u
f
a
c
tu
ri
n
g
,
v
o
l.
2
8
,
n
o
.
3
,
p
p
.
2
8
3
-
2
8
8
,
Au
g
.
2
0
1
5
.
[6
]
Y.
Nin
o
m
iy
a
,
Y
.
Arita,
R.
Ta
n
a
k
a
,
T.
Nish
i
d
a
a
n
d
N.
I.
G
ian
n
o
c
c
a
ro
,
"
A
u
to
m
a
ti
c
Ca
li
b
ra
ti
o
n
o
f
In
d
u
strial
Ro
b
o
t
a
n
d
3
D
S
e
n
so
rs
u
si
n
g
Re
a
l
-
Ti
m
e
S
i
m
u
lato
r,
"
2
0
1
8
I
n
ter
n
a
t
io
n
a
l
Co
n
fer
e
n
c
e
o
n
In
fo
rm
a
t
io
n
a
n
d
Co
mm
u
n
ica
ti
o
n
T
e
c
h
n
o
l
o
g
y
R
o
b
o
ti
c
s (ICT
-
ROBOT
)
,
p
p
.
1
-
4
,
2
0
1
8
.
[7
]
B.
M
u
,
J.
Ch
e
n
,
Y.
S
h
i
a
n
d
Y.
C
h
a
n
g
,
"
De
sig
n
a
n
d
Im
p
lem
e
n
tatio
n
o
f
No
n
u
n
i
fo
rm
S
a
m
p
li
n
g
Co
o
p
e
ra
ti
v
e
Co
n
tro
l
o
n
A
G
ro
u
p
o
f
Tw
o
-
Wh
e
e
led
M
o
b
i
le
Ro
b
o
ts,
"
IEE
E
T
r
a
n
sa
c
t
io
n
s
o
n
In
d
u
stri
a
l
El
e
c
tro
n
ics
,
v
o
l.
6
4
,
n
o
.
6
,
p
p
.
5
0
3
5
-
5
0
4
4
,
2
0
1
7
.
[8
]
O.
Ca
ll
a
,
S
.
M
a
th
u
r
a
n
d
K.
L.
G
a
d
ri,
"
P
o
ss
ib
le
Lan
d
i
n
g
site
fo
r
C
h
a
n
d
ra
y
a
a
n
-
2
Ro
v
e
r,
"
2
0
1
6
I
n
ter
n
a
ti
o
n
a
l
Co
n
fer
e
n
c
e
o
n
Rec
e
n
t
A
d
v
a
n
c
e
s
a
n
d
In
n
o
v
a
ti
o
n
s in
En
g
i
n
e
e
rin
g
(I
CRA
IE)
,
p
p
.
1
-
5
,
2
0
1
6
.
[9
]
J.
M
e
n
g
,
A.
Li
u
,
Y.
Ya
n
g
,
Z.
W
u
a
n
d
Q.
X
u
,
"
Two
-
Wh
e
e
led
Ro
b
o
t
P
latf
o
rm
Ba
se
d
o
n
P
ID
Co
n
t
ro
l,
"
2
0
1
8
5
t
h
In
ter
n
a
t
io
n
a
l
C
o
n
fer
e
n
c
e
o
n
I
n
fo
r
ma
ti
o
n
S
c
ien
c
e
a
n
d
Co
n
tro
l
En
g
i
n
e
e
rin
g
(ICI
S
CE)
,
p
p
.
1
0
1
1
-
1
0
1
4
,
2
0
1
8
.
[1
0
]
K.
P
iem
n
g
a
m
,
I.
Nilk
h
a
m
h
a
n
g
a
n
d
P
.
B
u
n
n
u
n
,
"
De
v
e
l
o
p
m
e
n
t
o
f
Au
to
n
o
m
o
u
s
M
o
b
il
e
Ro
b
o
t
P
latfo
rm
with
M
e
c
a
n
u
m
Wh
e
e
ls,"
2
0
1
9
Fi
rs
t
I
n
ter
n
a
t
io
n
a
l
S
y
mp
o
si
u
m
o
n
I
n
stru
me
n
t
a
ti
o
n
,
C
o
n
tro
l,
Arti
fi
c
ia
l
In
telli
g
e
n
c
e
,
a
n
d
R
o
b
o
ti
c
s
(ICA
-
S
Y
M
P)
,
p
p
.
9
0
-
93
,
2
0
1
9
.
[1
1
]
D.
Cu
i
,
X.
G
a
o
,
W.
G
u
o
a
n
d
H.
Do
n
g
,
"
De
sig
n
a
n
d
S
ta
b
il
it
y
An
a
ly
sis
o
f
a
Wh
e
e
l
-
Trac
k
R
o
b
o
t,
"
2
0
1
6
3
rd
In
ter
n
a
t
io
n
a
l
C
o
n
fer
e
n
c
e
o
n
I
n
fo
r
ma
ti
o
n
S
c
ien
c
e
a
n
d
Co
n
tro
l
En
g
i
n
e
e
rin
g
(ICI
S
CE)
,
p
p
.
9
1
8
-
9
2
2
,
2
0
1
6
.
[1
2
]
H
.
W
a
n
g
,
B
.
L
i
,
J
.
L
i
u
,
e
t
a
l
.
,
"
D
y
n
a
m
i
c
m
o
d
e
l
i
n
g
a
n
d
a
n
a
l
y
s
i
s
o
f
W
h
e
e
l
S
k
i
d
s
t
e
e
r
e
d
M
o
b
i
l
e
R
o
b
o
t
s
w
i
t
h
t
h
e
d
i
f
f
e
r
e
n
t
a
n
g
u
l
a
r
v
e
l
o
c
i
t
i
e
s
o
f
f
o
u
r
w
h
e
e
l
s
,
"
P
r
o
c
e
e
d
i
n
g
s
o
f
t
h
e
3
0
t
h
C
h
i
n
e
s
e
C
o
n
t
r
o
l
C
o
n
f
e
r
e
n
c
e
,
p
p
.
3
9
1
9
-
3924
,
2
0
1
1
.
[1
3
]
Z.
F
a
n
,
Q
.
Qiu
a
n
d
Z
.
M
e
n
g
,
"
Im
p
lem
e
n
tatio
n
o
f
a
f
o
u
r
-
w
h
e
e
l
d
riv
e
a
g
ricu
lt
u
ra
l
m
o
b
il
e
ro
b
o
t
fo
r
c
ro
p
/so
il
in
fo
rm
a
ti
o
n
c
o
ll
e
c
ti
o
n
o
n
th
e
o
p
e
n
field
,
"
2
0
1
7
3
2
n
d
Y
o
u
th
Aca
d
e
mic
An
n
u
a
l
Co
n
fer
e
n
c
e
o
f
Ch
i
n
e
se
Asso
c
ia
ti
o
n
o
f
Au
to
m
a
ti
o
n
(
Y
AC)
,
p
p
.
4
0
8
-
4
1
2
,
2
0
1
7
.
[1
4
]
K.
P
rian
d
a
n
a
,
e
t
a
l
.
,
"
De
sig
n
o
f
A
Tas
k
-
Orie
n
ted
A
u
to
n
o
m
o
u
s
Wh
e
e
led
-
R
o
b
o
t
f
o
r
S
e
a
rc
h
a
n
d
Re
sc
u
e
,
"
2
0
1
8
In
ter
n
a
t
io
n
a
l
C
o
n
fer
e
n
c
e
o
n
A
d
v
a
n
c
e
d
C
o
mp
u
ter
S
c
ien
c
e
a
n
d
I
n
f
o
rm
a
ti
o
n
S
y
ste
ms
(ICACS
I
S
)
,
p
p
.
2
5
9
-
2
6
3
,
2
0
1
8
.
[1
5
]
H.
Aa
g
e
la,
e
t
a
l
.
,
"
A
n
As
u
s_
x
ti
o
n
_
p
ro
b
a
se
d
i
n
d
o
o
r
M
AP
P
ING
u
sin
g
a
Ra
sp
b
e
rry
P
i
wit
h
T
u
rtl
e
b
o
t
ro
b
o
t
T
u
rtl
e
b
o
t
ro
b
o
t,
"
2
0
1
7
2
3
rd
I
n
ter
n
a
ti
o
n
a
l
C
o
n
fer
e
n
c
e
o
n
Au
to
ma
ti
o
n
a
n
d
Co
mp
u
ti
n
g
(IC
AC)
,
p
p
.
1
-
5
,
2
0
1
7
.
[1
6
]
R.
M
is
h
ra
a
n
d
A.
Ja
v
e
d
,
"
ROS
b
a
se
d
se
rv
ice
r
o
b
o
t
p
latfo
rm
,
"
201
8
4
t
h
I
n
ter
n
a
t
io
n
a
l
Co
n
fer
e
n
c
e
o
n
C
o
n
tr
o
l
,
Au
to
m
a
ti
o
n
a
n
d
R
o
b
o
ti
c
s (ICCA
R
)
,
p
p
.
5
5
-
59
,
2
0
1
8
.
[1
7
]
A
.
K
o
u
b
â
a
,
e
t
a
l
.
,
"
T
u
r
t
l
e
b
o
t
a
t
O
f
f
i
c
e
:
A
S
e
r
v
i
c
e
-
O
r
i
e
n
t
e
d
S
o
f
t
w
a
r
e
A
r
c
h
i
t
e
c
t
u
r
e
f
o
r
P
e
r
s
o
n
a
l
A
s
s
i
s
t
a
n
t
R
o
b
o
t
s
U
s
i
n
g
R
O
S
,
"
2
0
1
6
I
n
t
e
r
n
a
t
i
o
n
a
l
C
o
n
f
e
r
e
n
c
e
o
n
A
u
t
o
n
o
m
o
u
s
R
o
b
o
t
S
y
s
t
e
m
s
a
n
d
C
o
m
p
e
t
i
t
i
o
n
s
(
I
C
A
R
S
C
)
,
p
p
.
2
7
0
-
276
,
2
0
1
6
.
[1
8
]
M
.
M
.
Ka
ss
ir
a
n
d
M
.
P
a
lh
a
n
g
,
"
No
v
e
l
q
u
a
li
tativ
e
v
isu
a
l
o
d
o
m
e
try
f
o
r
a
g
ro
u
n
d
:
Ve
h
icle
b
a
se
d
o
n
fu
n
n
e
l
lan
e
c
o
n
c
e
p
t,
"
2
0
1
7
1
0
t
h
Ira
n
ia
n
Co
n
f
e
re
n
c
e
o
n
M
a
c
h
i
n
e
Vi
sio
n
a
n
d
Im
a
g
e
Pro
c
e
ss
in
g
(M
VIP
)
,
p
p
.
1
8
2
-
187
,
2
0
1
7
.
[1
9
]
Q.
Li
n
,
X.
Li
u
a
n
d
Z.
Zh
a
n
g
,
"
M
o
b
i
le
R
o
b
o
t
S
e
lf
-
L
o
c
a
li
z
a
ti
o
n
Us
in
g
Visu
a
l
Od
o
m
e
try
Ba
se
d
o
n
Ce
il
i
n
g
Visi
o
n
,
"
2
0
1
9
IE
EE
S
y
mp
o
si
u
m S
e
rie
s o
n
Co
mp
u
t
a
ti
o
n
a
l
I
n
telli
g
e
n
c
e
(S
S
CI
)
,
p
p
.
1
4
3
5
-
1
4
3
9
,
2
0
1
9
.
[2
0
]
M
.
G
a
ll
i,
R.
Ba
rb
e
r,
S
.
G
a
rrid
o
a
n
d
L.
M
o
re
n
o
,
"
P
a
th
p
lan
n
in
g
u
sin
g
M
a
tl
a
b
-
ROS
i
n
teg
ra
ti
o
n
a
p
p
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