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Dep
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I
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UCT
I
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h
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t
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y
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tio
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tics
m
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e
f
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le
[1
]
,
[
2]
.
E
ac
h
o
f
th
ese
m
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o
d
s
h
as
its
o
w
n
ad
v
an
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s
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d
a
y
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i
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[
3
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w
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cted
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b
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d
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4
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w
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d
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[
5
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w
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[
6
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,
th
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a
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a
m
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s
t
if
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f
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th
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esire
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n
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th
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lled
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P
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in
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t.
T
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is
an
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ased
o
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f
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if
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[
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.
T
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1117
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p
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[
10]
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V
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1
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en
t,
an
d
th
e
v
a
lu
e
s
w
il
l
b
e
u
s
ed
to
g
e
n
er
ate
p
ath
f
o
r
th
e
au
to
n
o
m
o
u
s
r
o
b
o
t
v
ia
a
m
eth
o
d
ca
lled
g
r
ad
ien
t
d
es
ce
n
t
s
ea
r
ch
(
GD
S)
m
et
h
o
d
.
T
h
e
ef
f
icie
n
c
y
o
f
th
e
HST
OR
-
9
P
m
et
h
o
d
w
ill
b
e
c
o
m
p
ar
ed
to
its
p
r
ed
ec
ess
o
r
ite
r
atio
n
m
et
h
o
d
s
,
i.e
.
s
u
cc
e
s
s
i
v
e
o
v
er
r
el
ax
atio
n
5
-
p
o
in
t
L
ap
lacia
n
(
SOR
-
5
P
)
,
ac
c
eler
ated
o
v
er
r
elax
atio
n
5
-
p
o
in
t
L
ap
lacia
n
(
A
O
R
-
5
P
)
,
tw
o
-
p
ar
a
m
eter
o
v
er
r
ela
x
atio
n
5
-
p
o
i
n
t
L
ap
lacia
n
(
T
OR
-
5
P
)
,
s
u
cc
es
s
iv
e
o
v
er
r
elax
a
tio
n
9
-
p
o
i
n
t
L
ap
lacia
n
(
SOR
-
9
P
)
,
ac
ce
ler
ated
o
v
er
r
e
lax
atio
n
9
-
p
o
in
t
L
ap
lacia
n
(
A
O
R
-
9
P
)
,
t
w
o
-
p
ar
a
m
eter
o
v
e
r
r
elax
atio
n
9
-
p
o
in
t
L
ap
lacia
n
(
T
OR
-
9
P
)
,
Half
-
S
w
ee
p
s
u
cc
e
s
s
i
v
e
o
v
er
r
elax
ati
o
n
5
-
p
o
in
t
L
ap
lacia
n
(
HS
SO
R
-
5
P
)
,
Half
-
S
w
ee
p
ac
ce
ler
ated
o
v
er
r
elax
atio
n
5
-
p
o
in
t
L
ap
lacia
n
(
HS
A
O
R
-
5
P
)
,
Half
-
S
w
ee
p
t
w
o
-
p
ar
a
m
e
ter
o
v
er
r
elax
atio
n
5
-
p
o
in
t
L
ap
lacia
n
(
HST
OR
-
5
P
)
,
Half
-
S
w
ee
p
s
u
cc
es
s
i
v
e
o
v
e
r
r
elax
atio
n
9
-
p
o
i
n
t
L
ap
lacia
n
(
HSSOR
-
9
P
)
,
an
d
Half
-
S
w
ee
p
ac
ce
ler
ated
o
v
er
r
elax
atio
n
9
-
p
o
in
t
L
ap
lacia
n
(
HS
A
OR
-
9
P
)
.
A
p
io
n
ee
r
i
n
g
s
t
u
d
y
co
n
d
u
cted
b
y
K
h
atib
[
1
2
]
in
tr
o
d
u
ce
d
th
e
ap
p
licatio
n
o
f
p
o
ten
tial
f
ield
s
i
n
s
o
l
v
in
g
th
e
p
r
o
b
le
m
o
f
r
o
b
o
t
p
at
h
p
lan
n
in
g
.
I
n
K
h
atib
’
s
w
o
r
k
,
t
h
e
co
n
ce
p
t
o
f
ar
ti
f
icial
p
o
te
n
tial
f
ield
(
A
P
F)
i
s
in
tr
o
d
u
ce
d
in
r
o
b
o
t
ar
m
’
s
o
p
er
atio
n
al
s
p
ac
e
.
Me
an
w
h
ile,
in
t
h
e
co
n
f
i
g
u
r
atio
n
s
p
ac
e,
o
b
s
tacle
s
p
u
l
s
ated
r
ep
ellin
g
f
o
r
ce
s
w
h
ile
t
h
e
o
b
j
e
ctiv
e
p
o
in
t
p
u
t
o
u
t
attr
ac
ti
v
e
f
o
r
ce
.
A
lt
h
o
u
g
h
p
o
ten
tial
f
ield
s
ee
m
ed
p
r
ac
ticab
le
in
p
ath
p
lan
n
i
n
g
,
i
t
d
o
es
s
u
f
f
er
f
r
o
m
t
h
e
u
n
f
o
r
tu
n
ate
cr
ea
t
io
n
o
f
s
p
u
r
io
u
s
lo
ca
l
m
i
n
i
m
a
w
h
ic
h
ca
u
s
ed
t
h
e
m
o
b
ile
r
o
b
o
t
to
f
all
in
to
p
r
e
m
at
u
r
e
s
tab
le
co
n
f
i
g
u
r
atio
n
b
ef
o
r
e
r
ea
ch
in
g
its
o
b
j
ec
tiv
e
p
o
in
t.
Ho
w
e
v
er
,
Ko
d
its
c
h
e
k
[
1
3
]
s
h
o
w
ed
t
h
at
t
h
is
m
i
g
h
t
n
o
t
b
e
th
e
ca
s
e,
at
l
ea
s
t
f
o
r
ce
r
tai
n
t
y
p
e
s
o
f
d
o
m
ai
n
.
No
n
et
h
eles
s
,
t
h
e
in
te
g
r
atio
n
o
f
A
P
F
w
it
h
s
u
ita
b
le
m
et
h
o
d
s
s
u
c
h
as
th
e
s
t
u
d
y
i
n
[
1
4
]
co
u
ld
p
r
o
v
id
e
b
en
ef
its
f
o
r
au
to
n
o
m
o
u
s
p
ath
p
lan
n
in
g
i
m
p
r
o
v
e
m
e
n
t.
Du
e
to
its
attr
ac
ti
v
e
p
r
o
p
er
ti
es,
h
ar
m
o
n
ic
f
u
n
ctio
n
s
ar
e
a
p
o
p
u
lar
ch
o
ice
in
s
o
lv
i
n
g
p
at
h
p
lan
n
in
g
p
r
o
b
lem
s
[
1
5
]
-
[
17]
.
T
h
r
o
u
g
h
h
ar
m
o
n
ic
f
u
n
ctio
n
s
,
w
h
ic
h
ar
e
also
s
o
lu
tio
n
s
to
L
ap
lace
’
s
eq
u
atio
n
,
n
o
lo
ca
l
m
i
n
i
m
u
m
i
s
g
e
n
er
ated
d
u
r
in
g
p
ath
p
la
n
n
in
g
co
m
p
u
ta
tio
n
s
[
9
]
,
[
1
8
]
.
S
o
m
e
o
t
h
er
n
o
tab
l
e
w
o
r
k
s
i
n
v
o
l
v
ed
t
h
e
ap
p
licatio
n
o
f
h
ar
m
o
n
ic
f
u
n
ctio
n
s
i
n
p
ath
p
la
n
n
i
n
g
i
n
cl
u
d
in
g
t
h
e
wo
r
k
b
y
W
a
y
d
o
an
d
Mu
r
r
a
y
[
1
9
]
w
h
er
e
t
h
e
y
in
v
e
s
tig
a
ted
th
e
u
s
e
o
f
s
tr
ea
m
f
u
n
ctio
n
s
o
n
m
o
tio
n
o
f
v
e
h
icle
s
.
Oth
er
th
a
n
t
h
at,
Szu
lcz
y
n
s
k
i
et
a
l.
[
2
0
]
h
ad
a
ls
o
ap
p
lied
h
ar
m
o
n
ic
f
u
n
ctio
n
s
in
t
h
eir
s
tu
d
y
o
n
it
s
ap
p
li
ca
tio
n
o
n
r
ea
l
-
ti
m
e
o
b
s
tacle
av
o
id
an
ce
.
B
y
u
s
in
g
f
i
n
ite
ele
m
e
n
ts
,
Gar
r
id
o
et
a
l.
[
9
]
ca
lcu
lated
h
ar
m
o
n
ic
f
u
n
c
tio
n
s
f
o
r
r
o
b
o
tics
l
o
co
m
o
tio
n
.
A
p
ar
t
f
r
o
m
th
a
t,
Dail
y
a
n
d
B
ev
l
y
[
2
1
]
ap
p
lied
an
aly
tical
p
o
ten
tial
f
u
n
ctio
n
s
f
o
r
s
o
l
v
i
n
g
co
m
p
lex
s
h
ap
ed
o
b
s
tacle
s
.
T
h
e
Half
-
S
w
ee
p
(
HS)
iter
atio
n
m
et
h
o
d
s
w
er
e
i
n
tr
o
d
u
ce
d
b
y
A
b
d
u
l
lah
[
2
2
]
w
h
e
re
it
w
a
s
u
s
ed
t
o
s
o
lv
e
2
D
P
o
is
s
o
n
eq
u
at
io
n
s
.
Sau
d
i
a
n
d
S
u
lai
m
an
[
2
3
]
in
tr
o
d
u
ce
d
th
e
ap
p
licatio
n
o
f
H
S
iter
atio
n
m
et
h
o
d
in
r
o
b
o
ti
cs
p
ath
p
lan
n
i
n
g
a
n
d
t
h
e
m
et
h
o
d
h
as
s
h
o
w
n
p
r
o
m
is
i
n
g
r
esu
lt
s
s
i
n
ce
[
2
4
]
,
[
2
5
]
.
Stan
d
ar
d
iter
atio
n
w
o
u
ld
e
m
p
lo
y
5
-
p
o
i
n
t
L
ap
lacia
n
.
Ho
w
e
v
er
,
th
e
9
-
p
o
in
t
L
ap
lacia
n
a
p
p
r
o
ac
h
is
u
s
ed
i
n
t
h
is
p
ap
er
t
o
g
en
er
ate
p
ath
f
o
r
m
o
b
ile
r
o
b
o
t.
Sau
d
i
a
n
d
S
u
lai
m
an
[
2
6
]
p
io
n
ee
r
ed
th
e
ap
p
lic
atio
n
o
f
9
-
p
o
i
n
t
L
ap
lacia
n
o
n
m
o
b
ile
r
o
b
o
t
p
ath
p
lan
n
i
n
g
.
A
d
a
m
et
a
l.
[
2
7
]
o
b
s
er
v
ed
th
at
f
o
r
a
s
m
o
o
t
h
in
i
tial
esti
m
atio
n
,
9
-
p
o
i
n
t
L
ap
la
cian
is
ex
p
ec
ted
to
h
av
e
m
o
r
e
ef
f
icie
n
t c
o
n
v
er
g
e
n
ce
r
ate
w
it
h
m
o
r
e
ac
cu
r
ate
co
m
p
u
tat
io
n
.
2.
T
H
E
H
E
A
T
T
RANSF
E
R
C
O
NCEPT
AN
D
M
O
DE
L
L
I
NG
O
F
E
NV
I
RO
NM
E
NT
S
T
h
e
en
v
ir
o
n
m
en
t
m
o
d
els
i
n
t
h
is
p
ap
er
ar
e
co
n
s
tr
u
cted
ac
co
r
d
in
g
l
y
to
tr
ea
t
t
h
e
p
ath
p
la
n
n
i
n
g
o
f
m
o
b
ile
r
o
b
o
t
as
a
s
tead
y
-
s
tate
h
ea
t
tr
an
s
f
er
p
r
o
b
le
m
.
B
y
s
o
lv
i
n
g
th
e
L
ap
lace
’
s
eq
u
at
io
n
m
o
d
elled
a
f
ter
th
i
s
h
ea
t
tr
an
s
f
er
p
r
o
b
lem
,
w
e
w
i
l
l
b
e
ab
le
to
g
et
th
e
tem
p
er
atu
r
e
v
alu
es
t
h
at
r
ep
r
esen
t
th
e
p
o
ten
tial
v
al
u
es
t
h
at
w
il
l
b
e
u
s
ed
b
y
th
e
m
o
b
ile
r
o
b
o
t
to
b
u
ild
a
s
m
o
o
th
an
d
u
n
o
b
s
tr
u
cted
p
ath
f
o
r
it
to
tr
av
el
o
n
.
Fo
r
th
is
to
w
o
r
k
,
th
e
e
n
v
ir
o
n
m
en
t
m
o
d
el
w
ill b
e
s
et
to
s
u
c
h
th
at
h
ea
t
w
il
l
f
lo
w
f
r
o
m
a
r
e
g
io
n
o
f
h
i
g
h
er
te
m
p
er
atu
r
e
to
r
eg
io
n
o
f
lo
w
er
te
m
p
er
at
u
r
e.
T
h
e
en
v
ir
o
n
m
e
n
t
m
o
d
el,
f
r
o
m
h
er
eo
n
to
b
e
ca
lled
th
e
co
n
f
i
g
u
r
atio
n
s
p
ac
e,
co
n
tain
s
w
it
h
i
n
it
th
e
e
x
ter
io
r
an
d
i
n
ter
io
r
b
o
u
n
d
ar
y
w
all
s
,
a
f
e
w
o
b
s
tacle
s
,
a
s
tar
tin
g
p
o
in
t
,
a
n
d
an
o
b
j
ec
t
iv
e
p
o
in
t
f
o
r
w
h
ic
h
th
e
m
o
b
ile
r
o
b
o
t
w
i
ll
b
e
h
ea
d
in
g
to
f
r
o
m
t
h
e
s
tar
ti
n
g
p
o
in
t.
T
h
e
m
o
b
ile
r
o
b
o
t
r
ep
r
esen
tatio
n
is
r
ed
u
ce
d
to
a
s
in
g
le
p
o
in
t
i
n
th
e
co
n
f
i
g
u
r
ati
o
n
s
p
ac
e.
B
o
u
n
d
ar
y
v
al
u
es
ar
e
d
ef
i
n
ed
b
y
t
h
e
Dir
ic
h
let
b
o
u
n
d
ar
y
co
n
d
itio
n
s.
T
h
e
o
u
ter
a
n
d
i
n
n
er
b
o
u
n
d
ar
y
w
all
s
,
to
g
eth
er
w
it
h
o
b
s
tacle
s
,
ar
e
ass
i
g
n
ed
h
ig
h
es
t
p
o
ten
tia
l
v
al
u
es
to
to
ac
t
as
h
ea
t
s
o
u
r
ce
,
w
h
ile
t
h
e
o
b
j
ec
tiv
e
p
o
in
t
is
a
s
s
i
g
n
ed
th
e
lo
w
e
s
t
p
o
ten
tial
p
o
in
t
to
ac
t
a
s
a
h
e
at
s
i
n
k
.
T
h
i
s
w
a
y
,
a
p
ath
w
ill
b
e
ab
le
to
b
e
p
r
o
d
u
ce
d
b
y
e
x
ec
u
ti
n
g
th
e
s
teep
es
t d
escen
t
s
ea
r
ch
,
th
at
is
GDS
o
n
t
h
e
p
o
ten
tia
l v
a
lu
e
s
.
GDS
is
a
s
ea
r
ch
t
h
at
f
o
llo
w
s
t
h
e
d
o
w
n
w
ar
d
s
lo
p
e
o
f
p
o
ten
ti
al
f
ield
s
f
r
o
m
h
i
g
h
to
lo
w
v
al
u
es
a
n
d
ca
n
b
e
u
s
ed
to
cr
ea
te
an
u
n
o
b
s
tr
u
cted
lin
e
o
f
p
ass
ag
e
f
r
o
m
it
s
s
tar
ti
n
g
p
o
in
t to
w
ar
d
s
its
d
esti
n
atio
n
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4752
I
n
d
o
n
esia
n
J
E
lec
E
n
g
&
C
o
m
p
Sci,
Vo
l.
22
,
No
.
2
,
Ma
y
2
0
2
1
:
1
1
1
6
-
1
1
2
3
1118
3.
H
ARM
O
NIC F
UNCTI
O
NS
Har
m
o
n
ic
f
u
n
ctio
n
s
w
i
ll
b
e
e
m
p
lo
y
ed
to
co
m
p
u
te
t
h
e
d
is
tr
ib
u
tio
n
o
f
te
m
p
er
atu
r
e
s
i
n
t
h
e
co
n
f
i
g
u
r
atio
n
s
p
ac
e.
I
n
m
at
h
e
m
atica
l
ter
m
s
,
a
h
ar
m
o
n
ic
f
u
n
ctio
n
o
n
t
h
e
d
o
m
ai
n
i
s
a
f
u
n
cti
o
n
th
at
s
ati
s
f
ie
s
t
h
e
L
ap
lace
’
s
eq
u
atio
n
:
∑
(
1
)
w
h
er
e
i
x
is
th
e
i
-
th
C
ar
tesi
a
n
co
o
r
d
in
ate
an
d
n
is
t
h
e
d
i
m
e
n
s
io
n
.
I
n
th
is
s
tu
d
y
,
th
e
d
o
m
ai
n
Ω
is
m
ad
e
u
p
o
f
ex
ter
io
r
an
d
in
ter
io
r
b
o
u
n
d
ar
y
w
alls
,
all
o
f
th
e
o
b
s
tacle
s
p
r
esen
t,
th
e
s
tar
tin
g
p
o
in
t,
an
d
th
e
o
b
j
ec
tiv
e
p
o
in
t.
T
o
s
o
lv
e
(
1
)
,
th
e
n
u
m
er
ical
m
et
h
o
d
HST
OR
-
9
P
w
ill
b
e
e
m
p
lo
y
ed
,
u
p
o
n
co
m
p
u
tatio
n
w
e
ca
n
o
b
tai
n
th
e
f
u
n
c
tio
n
v
al
u
es
r
elati
v
e
to
ea
ch
n
o
d
e.
As
f
o
r
th
e
b
o
u
n
d
ar
i
es
i
n
t
h
e
f
o
r
m
u
la,
Dir
ic
h
let
b
o
u
n
d
ar
y
co
n
d
itio
n
s
w
il
l b
e
u
s
ed
to
co
n
s
tr
ai
n
th
e
L
ap
lace
’
s
eq
u
atio
n
.
4.
T
H
E
F
O
RM
UL
AT
I
O
N
O
F
H
ST
O
R
-
9P
I
n
FS
iter
atio
n
,
t
h
e
w
h
o
le
c
o
m
p
u
tatio
n
al
g
r
id
is
ta
k
e
n
in
to
iter
ativ
e
co
m
p
u
ta
tio
n
,
an
d
th
e
n
o
d
al
f
u
n
ctio
n
s
ar
e
iter
ated
f
r
o
m
t
h
e
n
o
d
al
s
et
o
f
t
h
e
f
ir
s
t
p
o
in
t
to
t
h
e
n
e
x
t
n
o
d
al
s
et
w
h
ic
h
is
i
ts
n
eig
h
b
o
u
r
i
n
g
p
o
i
n
t
an
d
s
o
o
n
s
eq
u
en
tiall
y
.
Fo
r
H
S
iter
atio
n
,
o
n
l
y
h
al
f
o
f
th
e
co
m
p
u
tat
io
n
al
g
r
id
is
iter
ated
,
a
n
d
th
e
n
o
d
al
s
et
s
ar
e
co
m
p
u
ted
f
r
o
m
th
e
f
ir
s
t p
o
i
n
t
an
d
m
o
v
e
o
n
to
th
e
n
e
x
t
n
o
d
al
s
et
w
h
ic
h
m
o
v
es
in
a
r
o
tated
m
an
n
er
,
t
h
i
s
m
ea
n
s
th
e
iter
atio
n
s
k
ip
s
a
p
o
i
n
t
i
n
e
v
er
y
i
ter
atio
n
c
y
cle.
T
h
e
v
i
s
u
ali
s
atio
n
s
f
o
r
th
e
i
n
d
i
v
id
u
al
n
o
d
al
s
et
f
o
r
FS
a
n
d
HS iter
atio
n
ar
e
as s
h
o
w
n
i
n
F
ig
u
r
e
1
.
To
u
n
d
er
s
tan
d
t
h
e
9
-
p
o
in
t
L
a
p
lacia
n
v
is
u
all
y
,
w
e
co
m
p
ar
e
it
w
it
h
th
e
s
ta
n
d
ar
d
5
-
p
o
in
t
L
ap
lacia
n
m
o
d
el
a
n
d
s
h
o
w
t
h
e
d
is
ti
n
cti
o
n
b
et
w
ee
n
b
o
th
m
et
h
o
d
s
i
n
a
p
o
r
tio
n
o
f
co
n
f
ig
u
r
atio
n
s
p
ac
e,
as
s
h
o
w
n
in
Fig
u
r
e
2
.
I
n
o
n
e
iter
atio
n
c
y
c
le,
it
i
s
clea
r
th
a
t
t
h
e
H
S
9
-
p
o
in
t
L
ap
lacia
n
co
m
p
u
tes
m
o
r
e
p
o
in
t,
r
es
u
lti
n
g
i
n
in
cr
ea
s
ed
ac
c
u
r
ac
y
in
co
m
p
u
t
atio
n
al
r
es
u
lt
s
d
u
e
to
m
o
r
e
d
ata
av
ailab
le
i
n
a
c
y
cle
o
f
i
ter
atio
n
,
t
h
u
s
,
a
m
o
r
e
ef
f
icien
t c
o
n
v
er
g
e
n
ce
r
ate.
(
a)
(
b
)
Fig
u
r
e
1
.
T
h
e
9
-
p
o
in
t L
ap
lacia
n
n
o
d
al
s
et
s
f
o
r
;
(
a)
f
u
l
l
-
s
w
ee
p
;
(
b
)
h
alf
-
s
w
ee
p
r
esp
ec
tiv
el
y
(
a)
(
b
)
Fig
u
r
e
2
.
T
h
e
n
o
d
al
f
u
n
ct
io
n
c
o
m
p
u
tatio
n
al
m
o
d
el
(
in
b
r
ac
k
et)
in
p
o
r
tio
n
o
f
co
n
f
i
g
u
r
atio
n
s
p
ac
e
f
o
r
HS
;
(
a)
5
-
p
o
in
t L
ap
lacia
n
;
(
b
)
9
-
p
o
in
t
L
ap
lacia
n
r
esp
ec
tiv
el
y
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esia
n
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2502
-
4752
A
u
to
n
o
mo
u
s
p
a
th
p
la
n
n
in
g
th
r
o
u
g
h
a
p
p
lic
a
tio
n
o
f ro
ta
te
d
tw
o
-
p
a
r
a
mete
r
o
ve
r
r
ela
xa
tio
n
…
(
W
.
K
.
L
in
g
)
1119
T
o
p
u
t
HST
OR
-
9
P
in
to
f
o
r
m
u
latio
n
,
f
r
o
m
(
1
)
th
e
2
-
d
i
m
e
n
s
i
o
n
al
L
ap
lace
’
s
eq
u
a
tio
n
is
g
i
v
en
as:
(
2
)
T
o
c
o
n
s
tr
u
ct
th
e
s
ta
n
d
ar
d
HS
9
-
p
o
in
t
L
ap
lacia
n
i
ter
atio
n
f
o
r
m
u
latio
n
,
(
2
)
ca
n
b
e
d
is
cr
etis
ed
b
ased
o
n
co
m
p
u
tatio
n
al
m
o
d
el
ill
u
s
tr
ate
d
in
Fig
u
r
e
2
(
b
)
an
d
Fig
u
r
e
3
(
b
)
,
th
u
s
w
r
itte
n
as:
(
3
)
I
m
p
le
m
e
n
ti
n
g
a
w
e
ig
h
ted
p
ar
am
eter
,
ω
[
2
8
]
-
[
30]
in
to
(
3
)
,
th
e
f
o
r
m
u
la
f
o
r
HSS
OR
-
9
P
ca
n
b
e
d
ef
in
ed
as:
(
4
)
Fro
m
(
4
)
,
th
e
m
et
h
o
d
ca
n
b
e
f
u
r
th
er
e
n
h
an
ce
d
to
f
o
r
m
t
h
e
f
o
r
m
u
la
f
o
r
a
n
e
n
r
ich
ed
i
te
r
atio
n
o
f
th
e
s
a
m
e
o
v
er
r
elax
atio
n
iter
atio
n
f
a
m
il
y
,
w
it
h
HS
9
-
p
o
in
t
L
ap
lacia
n
ch
ar
ac
ter
is
tic
s
,
n
a
m
el
y
HS
AO
R
-
9
P
.
T
o
b
u
ild
th
e
f
o
r
m
u
latio
n
,
a
s
ec
o
n
d
w
ei
g
h
t
ed
p
ar
am
eter
,
r
,
is
e
x
tr
ac
ted
an
d
i
m
p
le
m
e
n
ted
in
to
(
4
)
.
B
y
r
ep
lacin
g
,
,
an
d
w
i
th
,
,
an
d
r
esp
ec
tiv
el
y
a
n
d
ad
d
in
g
th
e
t
e
r
m
s
,
,
,
an
d
,
w
e
o
b
tain
t
h
e
f
o
r
m
u
la
f
o
r
HS
AOR
-
9
P
as:
(
)
(
5
)
Fin
all
y
,
f
r
o
m
(
5
)
,
w
e
i
m
p
r
o
v
e
th
e
HS
A
O
R
-
9
P
iter
atio
n
b
y
d
r
a
w
i
n
g
o
u
t
a
t
h
ir
d
w
ei
g
h
t
ed
p
ar
am
eter
,
s
to
o
b
tain
th
e
f
o
r
m
u
latio
n
f
o
r
HST
OR
-
9
P
.
T
h
is
is
f
o
r
m
u
lated
b
y
r
ep
lacin
g
an
d
w
it
h
an
d
r
esp
ec
tiv
el
y
.
T
h
u
s
,
w
e
o
b
tain
:
(
6
)
B
y
ap
p
l
y
in
g
(
6
)
,
a
h
ar
m
o
n
ic
f
u
n
ct
io
n
t
h
at
s
o
lv
e
s
t
h
e
L
ap
lac
e’
s
eq
u
atio
n
in
to
co
m
p
u
tatio
n
,
w
e
o
b
tain
th
e
p
o
ten
tial
v
al
u
es
w
h
ic
h
w
i
ll
b
e
u
s
ed
b
y
th
e
m
o
b
ile
r
o
b
o
t
to
g
en
er
ate
p
ath
t
h
r
o
u
g
h
GDS
tech
n
iq
u
e.
T
h
e
v
alu
e
o
f
ω
is
d
eter
m
i
n
ed
th
r
o
u
g
h
s
e
n
s
i
tiv
it
y
a
n
al
y
s
i
s
an
d
is
s
et
at
an
d
th
e
v
al
u
e
s
f
o
r
w
e
i
g
h
te
d
p
ar
am
eter
r
an
d
s
ar
e
also
test
ed
th
r
o
u
g
h
s
en
s
iti
v
it
y
a
n
al
y
s
i
s
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d
g
en
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all
y
s
et
to
b
e
as
a
p
p
r
o
x
im
a
te
b
u
t
n
o
t
th
e
s
a
m
e
as
th
e
v
al
u
e
o
f
ω
[
3
1
]
.
T
o
en
s
u
r
e
th
at
t
h
e
co
m
p
u
tat
io
n
al
r
esu
lt
s
ca
n
av
o
id
f
lat
r
e
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n
in
ca
lcu
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tio
n
s
,
th
e
iter
atio
n
s
w
er
e
s
et
to
s
to
p
w
h
en
t
h
e
m
ax
i
m
u
m
er
r
o
r
o
f
th
e
s
o
lu
tio
n
s
f
all
s
i
n
s
id
e
t
h
e
r
an
g
e
o
f
s
p
ec
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ied
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ler
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r
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r
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eith
er
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-
16
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E
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m
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:
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m
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b
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s
: 2
,
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6
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ac
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3
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;
(
b
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E
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v
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m
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n
t 2
:
N
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: 3
,
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ied
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;
(
c)
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m
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t 3
:
Nu
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: 4
,
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6
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(
d
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E
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
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o
f
th
i
s
s
tu
d
y
RE
F
E
R
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NC
E
S
[1
]
H.
Y.
Zh
a
n
g
,
W
.
L
in
,
a
n
d
A
.
Ch
e
n
,
“
P
a
th
P
lan
n
in
g
f
o
r
T
h
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o
b
i
le
Ro
b
o
t:
A
Re
v
ie
w
,
”
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y
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l.
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3
3
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1
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0
0
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5
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.
[2
]
A
.
Ko
u
b
a
a
e
t
a
l.
,
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tr
o
d
u
c
ti
o
n
t
o
M
o
b
i
le
Ro
b
o
t
P
a
th
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la
n
n
i
n
g
,
”
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b
o
t
P
a
t
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a
n
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1
.
[3
]
A
.
S
.
Ha
n
d
a
y
a
n
i,
S
.
Nu
r
m
a
in
i,
I.
Ya
n
i,
a
n
d
N.
L
.
Hu
sn
i,
“
A
n
a
l
y
sis
o
n
S
w
a
r
m
Ro
b
o
t
Co
o
rd
in
a
ti
o
n
u
sin
g
F
u
z
z
y
L
o
g
ic,”
In
d
o
n
e
sia
n
J
o
u
rn
a
l
o
f
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e
c
trica
l
En
g
i
n
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rin
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n
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m
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.
[4
]
S
.
H.
A
b
d
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lred
a
h
a
n
d
D.
J.
Ka
d
h
im
,
“
De
v
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in
g
a
Re
a
l
T
i
m
e
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v
ig
a
ti
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n
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o
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il
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ts
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t
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k
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o
w
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v
iro
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m
e
n
ts,
”
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d
o
n
e
sia
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J
o
u
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n
a
l
o
f
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c
trica
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En
g
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e
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g
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mp
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ter
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e
,
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l.
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0
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o
.
1
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i
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p
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0
9
.
[5
]
S
.
K.
Da
s,
A
.
K.
Du
tt
a
,
a
n
d
S
.
K
.
De
b
n
a
th
,
“
Op
e
ra
ti
v
e
Crit
ica
l
P
o
i
n
tBu
g
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lg
o
rit
h
m
-
L
o
c
a
l
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a
th
P
la
n
n
i
n
g
o
f
M
o
b
il
e
Ro
b
o
t
A
v
o
id
in
g
Ob
sta
c
les
,
”
In
d
o
n
e
sia
n
J
o
u
r
n
a
l
o
f
El
e
c
trica
l
E
n
g
in
e
e
rin
g
a
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d
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o
mp
u
ter
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c
ien
c
e
,
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o
l.
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8
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o
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3
,
p
p
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6
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6
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c
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1
8
.
i3
.
p
p
1
6
4
6
-
1
6
5
6
.
[6
]
M
.
N.
Zaf
a
r,
a
n
d
J.
C.
M
o
h
a
n
ta,
“
M
e
th
o
d
o
l
o
g
y
f
o
r
P
a
th
P
lan
n
in
g
a
n
d
Op
ti
m
iza
ti
o
n
o
f
M
o
b
il
e
Ro
b
o
ts:
A
Re
v
i
e
w
,
”
Pro
c
e
d
ia
C
o
mp
u
ter
S
c
ien
c
e
,
v
o
l.
1
3
3
,
p
p
.
1
4
1
–
1
5
2
,
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0
1
8
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d
o
i:
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0
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1
0
1
6
/j
.
p
ro
c
s.2
0
1
8
.
0
7
.
0
1
8
.
[7
]
S
.
H.
A
.
W
a
h
a
b
,
A
.
Ch
e
k
i
m
a
,
N.
S
a
a
d
,
a
n
d
A
.
S
a
u
d
i
,
“
P
a
th
P
l
a
n
n
in
g
o
f
UA
V
b
a
se
d
o
n
F
lu
i
d
Co
m
p
u
ti
n
g
v
ia
A
c
c
e
lera
t
e
d
M
e
th
o
d
,
”
In
ter
n
a
ti
o
n
a
l
J
o
u
rn
a
l
o
f
E
n
g
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g
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re
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y
,
p
p
.
7
6
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2
0
2
0
.
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o
i
:
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0
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4
4
4
5
/2
2
3
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5
3
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T
I2
P
2
1
2
[8
]
C.
I.
C
o
n
n
o
ll
y
a
n
d
R.
A
.
G
ru
p
p
e
n
,
“
T
h
e
A
p
p
li
c
a
ti
o
n
s
o
f
Ha
rm
o
n
ic
F
u
n
c
ti
o
n
s
to
Ro
b
o
ti
c
s,”
J
o
u
r
n
a
l
o
f
Ro
b
o
t
ic
S
y
st
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m
s,
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o
.
7
,
p
p
.
9
3
1
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4
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0
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1
0
0
2
/ro
b
.
4
6
2
0
1
0
0
7
0
4
.
[9
]
S
.
G
a
rrid
o
,
L
u
is
M
o
re
n
o
,
D
o
lo
re
s
Blan
c
o
,
a
n
d
F
e
rn
a
n
d
o
M
a
r
tí
n
M
o
n
a
r,
“
Ro
b
o
ti
c
M
o
t
io
n
u
sin
g
Ha
r
m
o
n
ic
F
u
n
c
ti
o
n
s
a
n
d
F
i
n
it
e
El
e
m
e
n
ts,
”
J
o
u
rn
a
l
o
f
I
n
telli
g
e
n
t
a
n
d
Ro
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o
ti
c
S
y
ste
ms
,
v
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l.
5
9
,
p
p
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5
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–
7
3
,
2
0
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o
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:
1
0
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1
0
8
4
6
-
0
0
9
-
9
3
8
1
-
3
.
[1
0
]
S
.
P
a
n
a
ti
,
Ba
y
a
n
jarg
a
l
B
a
a
sa
n
d
o
rj
,
a
n
d
Kil
To
Ch
o
n
g
,
"
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u
to
n
o
m
o
u
s
M
o
b
il
e
Ro
b
o
t
Na
v
ig
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u
sin
g
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r
m
o
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ic
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o
ten
ti
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l
F
ield
,
"
IOP
Co
n
fer
e
n
c
e
S
e
rie
s:
M
a
ter
ia
ls
S
c
ien
c
e
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d
En
g
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n
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9
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8
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0
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2
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8
.
[1
1
]
S
.
S
a
sa
k
i,
“
A
P
ra
c
ti
c
a
l
Co
m
p
u
tatio
n
a
l
T
e
c
h
n
iq
u
e
f
o
r
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o
b
il
e
Ro
b
o
t
Na
v
ig
a
ti
o
n
,
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in
Pro
c
e
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d
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n
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s
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1
9
9
8
IEE
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In
ter
n
a
t
io
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a
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C
o
n
fer
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n
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C
o
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ro
l
A
p
p
li
c
a
ti
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n
s
,
v
o
l.
2
,
1
9
9
8
,
p
p
.
1
3
2
3
-
1
3
2
7
.
[1
2
]
O.
Kh
a
ti
b
,
“
Re
a
l
-
T
i
m
e
Ob
sta
c
l
e
s
Av
o
id
a
n
c
e
f
o
r
M
a
n
ip
u
lato
rs
a
n
d
M
o
b
il
e
Ro
b
o
t,
”
in
Pro
c
e
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d
in
g
s
o
f
I
EE
E
In
ter
n
a
t
io
n
a
l
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n
fer
e
n
c
e
o
n
Ro
b
o
ti
c
s
a
n
d
Au
to
ma
ti
o
n
,
v
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l.
2
,
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o
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1
,
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9
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p
p
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5
0
0
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5
0
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o
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7
8
-
1
-
4
6
1
3
-
8
9
9
7
-
2
_
29
.
[1
3
]
D.
E.
Ko
d
it
sc
h
e
k
,
“
Ex
a
c
t
Ro
b
o
t
Na
v
ig
a
ti
o
n
b
y
M
e
a
n
s
o
f
P
o
ten
ti
a
l
F
u
n
c
ti
o
n
s:
S
o
m
e
T
o
p
o
lo
g
ica
l
C
o
n
sid
e
ra
ti
o
n
s,”
in
Pro
c
e
e
d
i
n
g
s
o
f
IE
EE
In
ter
n
a
ti
o
n
a
l
Co
n
fer
e
n
c
e
o
n
Ro
b
o
t
ics
a
n
d
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to
m
a
ti
o
n
,
v
o
l.
4
,
1
9
8
7
,
p
p
.
1
-
6.
[1
4
]
E.
F
a
lo
m
ir,
S
.
Ch
a
u
m
e
tt
e
a
n
d
G
.
G
u
e
rrin
i,
“
A
3
D
M
o
b
il
it
y
M
o
d
e
l
f
o
r
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u
to
n
o
m
o
u
s
S
w
a
r
m
s
o
f
Co
ll
a
b
o
ra
ti
v
e
UA
V
s,
”
in
In
ter
n
a
ti
o
n
a
l
Co
n
fer
e
n
c
e
o
n
U
n
ma
n
n
e
d
Ai
rc
ra
ft
S
y
ste
ms
(
ICUAS
)
,
2
0
1
9
,
p
p
.
1
9
6
-
2
0
4
.
[1
5
]
A
.
M
a
so
u
d
a
n
d
A
.
A
l
-
S
h
a
ik
h
i,
“
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e
n
so
r
-
Ba
se
d
,
T
ime
-
Crit
ica
l
M
o
b
il
it
y
o
f
A
u
to
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o
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o
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s
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b
o
ts
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n
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tt
e
re
d
S
p
a
c
e
s:
A
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r
m
o
n
ic
P
o
ten
t
ial
A
p
p
ro
a
c
h
,
”
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o
ti
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a
,
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l.
3
6
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o
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7
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S
0
2
6
3
5
7
4
7
1
8
0
0
0
4
0
1
.
[1
6
]
A
.
F
a
lcó
,
L
.
Hilario
,
N.
M
o
n
tés
,
M
,
C.
M
o
ra
,
a
n
d
E.
Na
d
a
l
,
“
A
P
a
th
P
lan
n
in
g
A
lg
o
rit
h
m
fo
r
a
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n
a
m
ic
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v
iro
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m
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t
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se
d
o
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r
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p
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r
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n
e
ra
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z
e
d
De
c
o
m
p
o
siti
o
n
,
”
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a
th
e
ma
ti
c
s
,
v
o
l.
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n
o
.
1
2
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0
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o
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o
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rg
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3
3
9
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th
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2
2
2
4
5
.
[1
7
]
A
.
S
a
u
d
i
a
n
d
J.
S
u
laim
a
n
,
“
Ha
r
m
o
n
ic
P
a
th
P
lan
n
i
n
g
u
sin
g
Tw
o
-
S
tag
e
Ha
l
f
-
S
w
e
e
p
A
rit
h
m
e
ti
c
M
e
a
n
M
e
th
o
d
,
”
J
o
u
rn
a
l
o
f
C
o
mp
u
ter
S
c
ien
c
e
&
Co
mp
u
ta
ti
o
n
a
l
M
a
t
h
e
ma
ti
c
s
,
v
o
l.
9
,
n
o
.
2
,
p
p
.
2
3
-
2
7
,
2
0
1
9
,
d
o
i
:
1
0
.
2
0
9
6
7
/JCS
CM
.
2
0
1
9
.
0
2
.
0
0
2
.
[1
8
]
C.
I.
Co
n
n
o
ll
y
,
J.
B.
Bu
rn
s,
a
n
d
R.
W
e
iss
.
,
“
P
a
th
S
e
a
rc
h
i
n
g
u
sin
g
Lap
lac
e
'
s
Eq
u
a
ti
o
n
,
”
in
Pro
c
e
e
d
in
g
s
o
f
IE
EE
In
ter
n
a
t
io
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a
l
Co
n
fer
e
n
c
e
o
n
Ro
b
o
ti
c
s
a
n
d
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9
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M
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ter
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4
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2
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6
6
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Evaluation Warning : The document was created with Spire.PDF for Python.
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1123
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ń
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1
]
R.
Da
il
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d
D.
M
.
Be
v
ly
,
“
Ha
r
m
o
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Pro
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ric
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0
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8
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2
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.
[2
2
]
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A
b
d
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ll
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h
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“
T
h
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F
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ter
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8
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3
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.
[2
3
]
A
.
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a
n
d
J.
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u
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a
n
,
“
Ha
l
f
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)
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ter
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,
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4
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.
[2
4
]
A
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d
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u
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,
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Pro
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,
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5
]
A
.
S
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d
J.
S
u
laim
a
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,
“
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d
-
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trate
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ter
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ter
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T
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ter
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3
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2
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5
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.
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6
]
A
.
S
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u
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i
J.
S
u
laim
a
n
,
“
Blo
c
k
Itera
ti
v
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M
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th
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sin
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in
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,
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2
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p
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2
0
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,
2
0
1
0
.
[2
7
]
L
.
M.
A
d
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m
s
,
R.
J.
LeV
e
q
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e
,
a
n
d
D.
M
.
Y
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n
g
,
“
A
n
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8
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o
i:
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0
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1
1
3
7
/
0
7
2
5
0
6
6
.
[2
8
]
D.
M
.
Yo
u
n
g
,
I
ter
a
ti
v
e
S
o
lu
t
io
n
o
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L
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rg
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e
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r S
y
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ms
,
L
o
n
d
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n
:
A
c
a
d
e
m
ic P
re
ss
,
1
9
7
1
.
[2
9
]
D.
M
.
Yo
u
n
g
,
“
S
e
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o
n
d
-
De
g
re
e
Itera
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M
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th
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u
ti
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f
L
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g
e
L
in
e
a
r
S
y
ste
m
s
,”
J
o
u
rn
a
l
o
f
Ap
p
ro
x
im
a
ti
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T
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ry
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v
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l
.
5
,
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2
,
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3
7
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8
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1
9
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2
,
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1
0
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1
0
1
6
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0
2
1
-
9
0
4
5
(
7
2
)
9
0
0
3
6
-
6
.
[3
0
]
D.
M
.
Yo
u
n
g
,
“
Itera
ti
v
e
S
o
lu
ti
o
n
o
f
L
in
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a
r
S
y
ste
m
s
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m
F
in
it
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El
e
m
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n
t
T
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c
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n
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s,” W
h
it
e
m
a
n
,
J.R.
.
T
h
e
M
a
th
e
ma
ti
c
s o
f
Fi
n
it
e
El
e
me
n
ts
a
n
d
A
p
p
li
c
a
ti
o
n
s
,
L
o
n
d
o
n
:
Aca
d
e
mic
Pre
ss
,
v
o
l.
2
,
p
p
.
4
3
9
-
4
6
4
,
1
9
7
6
.
[3
1
]
A
.
H
a
d
ji
d
im
o
s,
“
A
c
c
e
lera
ted
O
v
e
rre
lax
a
ti
o
n
M
e
th
o
d
,
”
M
a
th
e
ma
ti
c
s
o
f
Co
mp
u
t
a
ti
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n
,
v
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l.
3
2
,
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.
1
4
1
,
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1
4
9
-
1
5
7
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1
9
7
8
,
d
o
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1
0
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1
0
9
0
/
S
0
0
2
5
-
5
7
1
8
-
1
9
7
8
-
0
4
8
3
3
4
0
-
6
.
[3
2
]
A
.
Zelin
sk
y
,
“
En
v
iro
n
m
e
n
t
Ex
p
lo
ra
t
io
n
a
n
d
P
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th
S
e
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rc
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A
l
g
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rit
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m
s
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M
o
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il
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R
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b
o
t
Na
v
ig
a
ti
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Us
in
g
S
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r,
”
D
o
c
to
ra
l
d
isse
rtatio
n
,
D
e
p
a
rtme
n
t
o
f
Co
m
p
u
ter
S
c
ien
c
e
,
Un
iv
e
rsity
o
f
W
o
ll
o
n
g
o
n
g
,
No
rth
f
ield
s
A
v
e
,
A
u
stra
li
a
,
1
9
9
1
.
[3
3
]
A
.
S
a
u
d
i,
“
Ro
b
o
t
P
a
t
h
P
la
n
n
i
n
g
u
sin
g
F
a
m
il
y
o
f
S
OR
Itera
ti
v
e
M
e
t
h
o
d
s
w
it
h
L
a
p
lac
ian
Be
h
a
v
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r
-
B
a
se
d
Co
n
tro
l,
”
P
h
.
D t
h
e
sis
,
Un
iv
e
rsiti
M
a
lay
sia
S
a
b
a
h
,
M
a
lay
sia
,
2
0
1
5
.
[3
4
]
A
.
A
.
Da
h
a
lan
,
M
.
S
.
M
u
th
u
v
a
lu
,
a
n
d
J.
S
u
laim
a
n
,
“
QSAG
E
Itera
ti
v
e
M
e
th
o
d
A
p
p
li
e
d
t
o
F
u
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z
y
P
a
ra
b
o
li
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Eq
u
a
ti
o
n
,
”
in
A
IP
Co
n
fer
e
n
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e
Pro
c
e
e
d
in
g
s
,
2
0
1
6
,
v
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l.
1
7
0
5
,
n
o
.
1
,
d
o
i
:
1
0
.
1
0
6
3
/
1
.
4
9
4
0
2
7
2
.
[3
5
]
A
.
A
.
D
a
h
a
lan
,
N.
S
.
A
.
A
z
iz,
a
n
d
J.
S
u
laim
a
n
,
“
P
e
rf
o
rm
a
n
c
e
o
f
Q
u
a
rter
-
S
w
e
e
p
S
u
c
c
e
ss
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v
e
O
v
e
rre
l
a
x
a
ti
o
n
it
e
ra
ti
v
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m
e
th
o
d
f
o
r
T
w
o
-
P
o
i
n
t
F
u
z
z
y
Bo
u
n
d
a
ry
V
a
lu
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P
ro
b
lem
s,”
J
o
u
rn
a
l
o
f
E
n
g
i
n
e
e
rin
g
a
n
d
Ap
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d
S
c
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n
c
e
s
,
v
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l.
1
1
,
n
o
.
7
,
p
p
.
1
4
5
6
-
1
4
6
3
,
2
0
1
6
,
d
o
i:
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0
.
3
9
2
3
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e
a
sc
i.
2
0
1
6
.
1
4
5
6
.
1
4
6
3
.
[3
6
]
A
.
A
.
Da
h
a
lan
,
N.
S
.
A
.
A
z
i
z
,
a
n
d
J.
S
u
laim
a
n
,
“
QSAG
E
Iter
a
ti
v
e
M
e
th
o
d
f
o
r
N
u
m
e
rica
l
S
o
lu
ti
o
n
o
f
Tw
o
-
P
o
in
t
F
u
z
z
y
Bo
u
n
d
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ry
V
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lu
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ro
b
lem
,
”
J
o
u
rn
a
l
o
f
En
g
i
n
e
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rin
g
a
n
d
A
p
p
li
e
d
S
c
ien
c
e
s
,
v
o
l.
1
2
,
n
o
.
1
6
,
p
p
.
4
0
6
8
-
4
0
7
4
,
2
0
1
7
,
d
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