I
nd
o
ne
s
ia
n J
o
urna
l o
f
E
lect
rica
l En
g
ineering
a
nd
Co
m
pu
t
er
Science
Vo
l.
24
,
No
.
2
,
N
o
v
em
b
e
r
2
0
2
1
,
p
p
.
1
0
1
7
~
1
0
2
6
I
SS
N:
2
5
0
2
-
4
7
5
2
,
DOI
: 1
0
.
1
1
5
9
1
/ijeecs.v
24
.i
2
.
p
p
1
0
1
7
-
1
0
2
6
1017
J
o
ur
na
l ho
m
ep
a
g
e
:
h
ttp
:
//ij
ee
cs.ia
esco
r
e.
co
m
Rev
iew on pa
t
h p
la
nning
alg
o
rithm f
o
r unma
nn
ed
a
eria
l
v
ehicles
Nurul Sa
li
ha
Am
a
ni I
bra
hi
m
,
F
a
iz
Asra
f
Sa
pa
rudin
De
p
a
rtme
n
t
o
f
El
e
c
tri
c
a
l
E
n
g
i
n
e
e
rin
g
Tec
h
n
o
lo
g
y
,
F
a
c
u
lt
y
o
f
En
g
i
n
e
e
rin
g
Tec
h
n
o
lo
g
y
,
Un
i
v
e
rsity
T
u
n
H
u
ss
e
in
On
n
M
a
lay
sia
,
M
a
lay
sia
Art
icle
I
nfo
AB
S
T
RAC
T
A
r
ticle
his
to
r
y:
R
ec
eiv
ed
Feb
24
,
2
0
2
1
R
ev
is
ed
Au
g
30
,
2
0
2
1
Acc
ep
ted
Sep
6
,
2
0
2
1
Th
e
p
a
th
p
la
n
n
i
n
g
p
ro
b
lem
h
a
s
b
e
e
n
a
c
ru
c
ial
to
p
ic
t
o
b
e
so
lv
e
d
in
a
u
to
n
o
m
o
u
s
v
e
h
icle
s.
P
a
th
p
lan
n
i
n
g
c
o
n
sists
o
p
e
ra
ti
o
n
s
to
fin
d
t
h
e
ro
u
te
th
a
t
p
a
ss
e
s
th
ro
u
g
h
a
ll
o
f
th
e
p
o
in
ts
o
f
in
tere
st
in
a
g
i
v
e
n
a
re
a
.
S
e
v
e
ra
l
a
lg
o
rit
h
m
s
h
a
v
e
b
e
e
n
p
ro
p
o
se
d
a
n
d
o
u
tl
i
n
e
d
in
th
e
v
a
rio
u
s
li
tera
tu
re
f
o
r
th
e
p
a
t
h
p
lan
n
in
g
o
f
a
u
to
n
o
m
o
u
s
v
e
h
icl
e
e
sp
e
c
ially
fo
r
u
n
m
a
n
n
e
d
a
e
ri
a
l
v
e
h
icle
s
(UA
V).
Th
e
a
l
g
o
rit
h
m
s
a
re
n
o
t
g
u
a
ra
n
tee
d
t
o
g
iv
e
fu
ll
p
e
rf
o
rm
a
n
c
e
in
e
a
c
h
p
a
th
p
lan
n
in
g
c
a
se
s
b
u
t
e
a
c
h
o
n
e
o
f
th
e
m
h
a
s
t
h
e
ir
o
wn
s
p
e
c
ifi
c
a
ti
o
n
wh
ich
m
a
k
e
s
th
e
m
su
it
a
b
le
in
so
p
h
isti
c
a
ted
situ
a
ti
o
n
.
T
h
is
re
v
iew
p
a
p
e
r
e
v
a
lu
a
tes
se
v
e
ra
l
p
o
ss
i
b
le
d
iffere
n
t
p
a
t
h
p
la
n
n
i
n
g
a
p
p
ro
a
c
h
e
s
o
f
UA
Vs
in
term
s
o
p
ti
m
a
l
p
a
th
,
p
ro
b
a
b
il
isti
c
c
o
m
p
lete
n
e
ss
a
n
d
c
o
m
p
u
tat
io
n
ti
m
e
a
lo
n
g
wit
h
th
e
ir
a
p
p
li
c
a
ti
o
n
in
sp
e
c
ifi
c
p
r
o
b
l
e
m
s.
K
ey
w
o
r
d
s
:
Alg
o
r
ith
m
Dr
o
n
e
Path
p
lan
n
in
g
Un
m
an
n
ed
ae
r
ial
v
eh
icles
T
h
is i
s
a
n
o
p
e
n
a
c
c
e
ss
a
rticle
u
n
d
e
r th
e
CC B
Y
-
SA
li
c
e
n
se
.
C
o
r
r
e
s
p
o
nd
ing
A
uth
o
r
:
Nu
r
u
l Salih
a
Am
an
i I
b
r
ah
im
Facu
lty
o
f
E
n
g
in
ee
r
in
g
T
ec
h
n
o
lo
g
y
Un
iv
er
s
iti T
u
n
Hu
s
s
ein
On
n
Ma
lay
s
ia
Pag
o
h
Hig
h
er
E
d
u
ca
tio
n
Hu
b
,
8
4
6
0
0
Pag
o
h
,
Mu
ar
,
J
o
h
o
r
,
M
alay
s
ia
E
m
ail: salih
aa
m
an
i9
6
@
g
m
ail.
co
m
1.
I
NT
RO
D
UCT
I
O
N
Ov
er
th
e
p
ast
d
ec
ad
es,
u
n
m
an
n
ed
ae
r
ial
v
eh
icles
(
UAVs)
h
a
v
e
in
cr
ea
s
in
g
ly
b
ee
n
ap
p
lied
in
d
if
f
e
r
en
t
ar
ea
s
with
wid
e
r
an
g
e
o
f
ap
p
licatio
n
s
,
s
u
ch
as
co
m
m
u
n
icatio
n
,
s
u
r
v
eillan
ce
,
p
h
o
to
g
r
am
m
etr
y
,
d
is
aster
m
an
ag
em
en
t,
a
n
d
s
tr
u
ctu
r
e
s
u
p
er
v
is
io
n
[
1
]
.
I
n
tellig
en
t
v
eh
icles
s
u
ch
as
UAV
s
h
av
e
ad
v
an
ce
d
th
eir
ca
p
ab
ilit
ies
f
o
r
h
ig
h
ly
a
n
d
,
ev
en
f
u
lly
,
au
t
o
m
ated
d
r
i
v
in
g
u
n
d
er
co
n
tr
o
lled
en
v
ir
o
n
m
en
ts
[
2
]
.
Ho
wev
er
,
p
at
h
p
lan
n
in
g
r
em
ain
s
o
n
e
o
f
th
e
p
r
im
ar
y
is
s
u
es
th
at
m
u
s
t
b
e
a
d
d
r
ess
ed
b
ef
o
r
e
v
eh
icles
ca
n
tr
av
er
s
e
in
c
o
m
p
lex
en
v
ir
o
n
m
en
ts
in
d
ep
e
n
d
en
tly
[
3
]
.
I
n
d
ee
d
,
o
n
e
o
f
th
e
m
o
s
t
d
if
f
icu
lt
p
r
o
b
lem
s
is
g
en
er
atin
g
an
ef
f
icien
t
p
ath
f
r
o
m
a
g
iv
en
in
itial
d
esti
n
ati
o
n
to
a
f
in
al
d
esti
n
atio
n
in
r
e
al
tim
e
[
4
]
.
T
h
u
s
,
m
u
ltip
le
al
g
o
r
ith
m
s
a
r
e
b
ein
g
in
tr
o
d
u
ce
d
an
d
im
p
r
o
v
ed
in
o
r
d
er
f
o
r
it to
b
e
ab
le
to
ch
o
o
s
e
th
e
p
ath
th
at
tak
es less
t
im
e
an
d
th
at
p
r
esen
ts
les
s
co
s
ts
to
ac
co
m
p
lis
h
th
e
in
ten
d
ed
task
s
[
5
]
.
−
Path
p
lan
n
in
g
ap
p
r
o
ac
h
T
h
is
r
ev
iew
p
ap
er
ev
alu
ates
s
ev
er
al
p
o
s
s
ib
le
p
ath
p
la
n
n
in
g
alg
o
r
ith
m
s
o
f
UAVs
in
te
r
m
s
o
p
tim
al
p
ath
,
p
r
o
b
ab
ilis
tic
co
m
p
leten
ess
an
d
co
m
p
u
tatio
n
tim
e
alo
n
g
with
th
eir
ap
p
licatio
n
in
s
p
ec
if
ic
p
r
o
b
le
m
s
.
T
h
ese
p
r
o
p
er
ties
ar
e
im
p
o
r
tan
t
in
p
ath
p
lan
n
in
g
alg
o
r
ith
m
,
wh
en
a
s
ea
r
ch
alg
o
r
ith
m
p
o
s
s
ess
es
th
e
p
r
o
p
er
ty
o
f
o
p
tim
ality
,
it
g
u
ar
an
tees
th
at
it
will
lo
ca
te
th
e
b
est
p
o
s
s
i
b
le
s
o
lu
tio
n
.
W
h
en
a
s
ea
r
ch
alg
o
r
ith
m
h
as
th
e
p
r
o
p
er
t
y
o
f
p
r
o
b
ab
ilis
tic
co
m
p
leten
es
s
,
it
m
ea
n
s
th
at
th
e
alg
o
r
ith
m
will
r
etu
r
n
a
s
o
lu
tio
n
if
o
n
e
is
av
ailab
le.
Path
p
lan
n
in
g
ap
p
r
o
ac
h
es
r
ev
iewe
d
in
th
is
p
ap
er
ca
n
b
e
class
if
ied
in
to
th
r
ee
ca
teg
o
r
ies;
Gr
ap
h
s
ea
r
ch
;
Sam
p
lin
g
-
B
ased
; a
n
d
B
io
lo
g
ic
al
-
I
n
s
p
ir
ed
Path
Plan
n
i
n
g
as illu
s
tr
ate
d
in
Fig
u
r
e
1.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
5
2
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci,
Vo
l.
24
,
No
.
2
,
No
v
em
b
er
2
0
2
1
:
1
0
1
7
-
1
0
2
6
1018
Fig
u
r
e
1
.
Path
p
lan
n
in
g
ap
p
r
o
ac
h
2.
G
RAP
H
SE
ARCH
I
n
g
r
ap
h
s
ea
r
ch
alg
o
r
ith
m
,
th
e
b
asic
id
ea
is
to
m
o
v
e
ac
r
o
s
s
f
r
o
m
p
o
i
n
t
A
to
p
o
in
t
B
in
s
o
m
e
s
o
r
t
o
f
s
p
ac
e.
T
h
is
s
tate
s
p
ac
e
is
co
m
m
o
n
ly
d
escr
ib
e
d
as
an
o
cc
u
p
a
n
cy
g
r
id
o
r
lattice
wh
ich
s
h
o
w
s
wh
er
e
o
b
jects
ar
e
lo
ca
ted
in
th
e
en
v
ir
o
n
m
e
n
t.
Gr
ap
h
b
ased
alg
o
r
ith
m
s
g
en
er
ally
im
p
lem
en
ted
i
n
s
p
ar
s
e
an
d
d
is
cr
ete
en
v
ir
o
n
m
en
t
[
6
]
.
Acc
o
r
d
in
g
t
o
[
2
]
,
s
o
m
e
o
f
th
e
au
to
m
ate
d
v
e
h
icle’
s
d
ev
el
o
p
m
en
t
h
as
b
ee
n
a
p
p
ly
in
g
th
is
m
eth
o
d
in
t
h
eir
p
ath
p
lan
n
in
g
p
r
o
ce
s
s
.
T
h
er
e
ar
e
s
ev
e
r
al
g
r
a
p
h
s
ea
r
ch
alg
o
r
ith
m
s
s
u
ch
as
A*
,
Dijk
s
tr
a’
s
,
an
d
D*
Alg
o
r
ith
m
.
2
.
1
.
Dij
k
s
t
ra
a
lg
o
rit
h
m
Dijk
s
tr
a
alg
o
r
ith
m
was
f
ir
s
t
in
tr
o
d
u
ce
d
in
1
9
5
6
b
y
E
d
s
g
er
W
.
Dijk
s
tr
a
[
7
]
.
I
t
is
b
ased
o
n
g
r
a
p
h
s
ea
r
ch
alg
o
r
ith
m
th
at
s
u
itab
le
in
f
in
d
in
g
s
in
g
le
-
s
o
u
r
ce
s
h
o
r
t
est
p
ath
b
etwe
en
o
n
e
n
o
d
e
an
d
ev
er
y
o
th
er
n
o
d
e
in
t
h
e
g
r
ap
h
u
s
in
g
g
r
ee
d
y
m
e
th
o
d
[
3
]
.
I
n
f
ac
t,
in
[
8
]
,
Dijk
s
tr
a
alg
o
r
ith
m
is
v
iewe
d
an
d
p
r
esen
ted
as
Gr
ee
d
y
alg
o
r
ith
m
.
T
h
e
m
o
s
t
d
is
tin
g
u
i
s
h
in
g
asp
ec
t
o
f
Dijk
s
tr
a
i
s
th
at
th
e
g
en
er
ated
p
ath
will
s
ta
r
t
at
th
e
ce
n
ter
an
d
th
en
ex
ten
d
e
d
to
th
e
en
d
p
o
in
t
in
th
e
e
n
v
ir
o
n
m
en
t.
T
h
e
f
o
r
m
u
latio
n
o
f
th
e
s
h
o
r
test
p
ath
b
etwe
en
th
at
v
er
te
x
an
d
e
v
er
y
o
th
e
r
v
e
r
tex
i
n
th
e
g
iv
en
e
n
v
ir
o
n
m
en
t
is
d
eter
m
i
n
ed
b
y
t
h
e
v
er
tex
(
n
o
d
es)
a
n
d
ed
g
es.
T
h
e
v
ar
y
i
n
g
weig
h
t
o
f
th
e
g
iv
en
a
r
ea
will
in
f
lu
en
ce
th
e
ch
o
ice
o
f
th
e
s
h
o
r
test
p
ath
in
th
e
e
n
v
ir
o
n
m
en
t
.
I
t
is
n
o
n
-
h
eu
r
is
tic
ap
p
r
o
ac
h
.
I
t
ca
n
p
r
o
v
id
e
s
h
o
r
test
p
ath
b
u
t
ca
n
n
o
t
p
r
o
m
is
e
o
p
tim
al
r
esu
lt
i
n
ter
m
s
o
f
tr
a
v
el
tim
e
as
i
n
[
9
]
.
Alth
o
u
g
h
th
at,
r
esu
lts
in
[
1
0
]
,
wh
ich
co
m
p
a
r
es
Dijk
s
tr
a’
s
,
A*
,
an
d
an
t
co
lo
n
y
o
p
tim
izatio
n
(
AC
O
)
s
h
o
ws
th
a
t
Dijk
s
tr
a
is
s
ti
ll
ab
le
to
g
iv
e
f
air
p
er
f
o
r
m
a
n
ce
in
r
ea
l
tim
e
p
ath
p
lan
n
in
g
b
y
h
av
in
g
th
e
least
r
u
n
tim
e.
B
y
in
te
g
r
atin
g
Dijk
s
tr
a’
s
with
V
o
r
o
n
o
i
d
iag
r
am
an
d
v
is
ib
ilit
y
al
g
o
r
ith
m
in
[
1
1
]
,
it
h
as
b
ee
n
p
r
o
v
en
t
o
s
av
e
u
p
t
o
2
1
%
o
f
en
er
g
y
,
wh
ich
is
en
er
g
y
ef
f
icie
n
t,
an
d
th
e
p
ath
ca
n
k
ee
p
th
e
v
eh
icle
f
r
o
m
co
llis
io
n
.
I
n
[
1
2
]
,
Dijk
s
tr
a’
s
alg
o
r
ith
m
b
ei
n
g
im
p
r
o
v
e
d
to
o
n
ly
co
n
s
id
er
th
e
n
ea
r
est
n
o
d
e
in
a
g
iv
e
n
en
v
i
r
o
n
m
e
n
t,
th
u
s
r
ed
u
cin
g
o
v
er
al
l
tim
e
co
n
s
u
m
p
tio
n
.
Acc
o
r
d
in
g
to
[
1
3
]
,
th
e
class
ical
Dijk
s
tr
a
alg
o
r
ith
m
o
n
ly
ca
p
ab
le
o
f
f
in
d
in
g
o
n
e
s
h
o
r
test
p
ath
,
wh
ile
s
k
ip
p
in
g
o
v
er
th
e
o
th
er
p
ath
s
with
t
h
e
e
q
u
al
d
is
tan
ce
.
T
h
u
s
,
to
ad
d
r
ess
th
is
is
s
u
e,
a
n
ew
e
n
h
an
ce
d
Dijk
s
tr
a
alg
o
r
ith
m
is
p
r
esen
ted
th
at
ab
le
to
f
in
d
all
s
h
o
r
test
p
ath
s
.
T
h
e
id
ea
l
p
ath
with
th
e
s
h
o
r
test
d
is
tan
ce
an
d
tim
e
is
f
o
u
n
d
b
y
ad
d
in
g
t
h
e
r
u
n
n
i
n
g
tim
e
in
t
h
e
p
ath
p
lan
n
in
g
ev
al
u
atio
n
.
2
.
2
.
A
*
a
lg
o
rit
hm
A*
i
s
an
ex
p
an
s
io
n
o
f
Dijk
s
tr
a’
s
g
r
ap
h
s
ea
r
ch
alg
o
r
ith
m
[
2
]
in
ad
d
r
ess
in
g
th
e
s
h
o
r
tco
m
in
g
o
f
Dijk
s
tr
a’
s
a
lg
o
r
ith
m
an
d
a
d
o
p
t
th
e
o
p
tim
u
m
p
r
io
r
ity
s
ea
r
ch
m
eth
o
d
[
1
4
]
.
A*
alg
o
r
ith
m
ca
n
d
eliv
er
a
g
en
er
al
h
eu
r
is
tic
ap
p
r
o
ac
h
in
th
e
p
r
o
c
ess
o
f
s
ea
r
ch
in
g
f
o
r
th
e
id
ea
l p
ath
[
7
]
.
C
o
m
p
a
r
ed
to
Dijk
s
tr
a
alg
o
r
ith
m
,
A*
h
av
e
h
ig
h
er
p
at
h
s
ea
r
ch
ef
f
icien
c
y
[
3
]
but
it
h
as
lo
n
g
er
co
m
p
u
tatio
n
tim
e
[
10]
b
u
t
b
o
t
h
o
f
th
em
h
as
f
air
p
er
f
o
r
m
an
ce
an
d
ca
n
b
e
im
p
l
em
en
ted
to
ac
co
m
p
lis
h
ed
r
ea
l
-
tim
e
p
ath
p
lan
n
in
g
.
T
h
e
s
ea
r
ch
alg
o
r
ith
m
th
at
u
s
ed
b
y
A*
is
b
est
-
f
ir
s
t
s
ea
r
c
h
[
1
5
]
.
T
h
e
d
if
f
er
en
ce
b
etwe
en
Dijk
s
tr
a’
s
an
d
b
est
-
f
ir
s
t
s
ea
r
ch
is
th
at
Dijk
s
tr
a
'
s
alg
o
r
ith
m
f
av
o
r
s
to
s
ea
r
ch
f
o
r
n
o
d
es
n
ea
r
th
e
in
itial
p
o
in
t
,
wh
ile
a
b
est
-
f
ir
s
t
s
ea
r
ch
f
av
o
r
s
n
o
d
e
n
ea
r
th
e
d
esti
n
atio
n
p
o
in
t.
A*
ac
ts
to
b
alan
ce
th
e
two
s
o
lu
tio
n
s
to
en
s
u
r
e
th
at
th
e
n
o
d
e
with
th
e
lo
west
tr
av
er
s
e
co
s
t
is
s
elec
ted
at
ev
er
y
lev
el.
Acc
o
r
d
in
g
to
[
2
]
,
A*
ca
n
n
o
t
ac
h
iev
e
co
n
tin
u
o
u
s
p
ath
b
u
t
it
e
n
s
u
r
es
th
at
th
e
s
h
o
r
test
r
o
u
te
is
alwa
y
s
f
o
llo
wed
in
th
e
d
ir
ec
tio
n
o
f
th
e
tar
g
et
n
o
d
e
[
3
]
.
T
h
e
m
o
d
if
ied
A*
a
lg
o
r
ith
m
h
as
b
ee
n
im
p
lem
en
ted
in
v
ar
io
u
s
p
ath
p
lan
n
in
g
a
p
p
licatio
n
[
1
5
]
-
[
1
9
]
.
2
.
3
.
D
*
a
lg
o
rit
hm
Sten
tz
[
2
0
]
is
th
e
f
ir
s
t
to
in
tr
o
d
u
ce
d
y
n
am
ic
A*
(
D*
)
.
I
t'
s
a
m
o
d
if
ied
v
er
s
io
n
o
f
A*
th
at'
s
b
ee
n
p
r
o
g
r
a
m
m
ed
to
s
wif
tly
r
ep
air
s
o
lu
tio
n
s
wh
en
t
h
e
s
tr
u
ctu
r
e
c
h
an
g
es.
At
ea
c
h
s
tate
in
th
e
tr
av
er
s
e,
an
o
p
tim
al
p
ath
to
th
e
g
o
al
is
ac
h
iev
ed
,
p
r
o
v
id
in
g
all
k
n
o
w
n
in
f
o
r
m
atio
n
at
ea
ch
s
tep
is
co
r
r
ec
t
[
2
1
]
.
W
h
en
th
e
n
o
d
es
in
P
at
h
P
l
an
ni
ng
A
pp
r
o
ac
h
G
r
ap
h
S
e
ar
c
h
D
i
jk
st
r
a
A
l
g
o
r
i
t
hm
A
*
Al
g
o
r
i
t
hm
D
*
Al
g
o
r
i
t
hm
S
am
pl
i
ng
B
as
e
d
R
ap
i
dl
y
-
E
xpl
o
r
i
ng
R
an
do
m
T
r
e
e
(
R
R
T
)
P
r
o
ba
bi
l
i
st
i
c
R
o
ad
m
ap
M
e
t
ho
d
(
P
R
M
)
B
i
o
l
o
g
i
c
al
l
y
Ins
pi
r
e
d
G
e
ne
t
i
c
A
l
g
o
r
i
t
hm
(
G
A
)
P
ar
t
i
c
l
e
S
war
m
O
pt
i
m
i
z
at
i
o
n
(
P
S
O
)
A
nt
Col
o
ny
O
pt
i
m
i
z
at
i
o
n
(
A
C
O
)
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2502
-
4
7
5
2
R
ev
iew
o
n
p
a
th
p
la
n
n
in
g
a
lg
o
r
ith
m
fo
r
u
n
ma
n
n
ed
a
eria
l v
eh
icles
(
N
u
r
u
l S
a
lih
a
A
ma
n
i I
b
r
a
h
im
)
1019
th
e
g
r
ap
h
ch
a
n
g
e,
o
n
ly
th
e
n
ew
co
s
ts
o
f
th
e
n
o
d
es
ar
e
u
p
d
ated
,
allo
win
g
th
e
p
r
io
r
p
a
th
to
b
e
ex
p
lo
ited
.
B
ec
au
s
e
it
d
o
es
n
o
t
h
av
e
to
r
e
-
p
lan
th
e
en
tire
p
ath
th
r
o
u
g
h
t
h
e
en
d
,
D*
ex
ten
d
s
f
ewe
r
n
o
d
es
as
co
m
p
ar
ed
to
A*
.
T
h
e
an
aly
s
is
o
f
a
n
o
d
e'
s
n
eig
h
b
o
r
s
is
u
s
ed
to
d
eter
m
in
e
th
e
co
s
t
o
f
m
o
tio
n
f
r
o
m
th
e
cu
r
r
en
t
n
o
d
e
to
th
e
n
eig
h
b
o
r
[
2
2
]
.
D*
is
m
o
s
t
ef
f
icien
t
wh
en
th
ese
ch
an
g
es
o
f
th
e
n
o
d
es
ar
e
d
etec
ted
n
ea
r
t
h
e
cu
r
r
en
t
s
tar
tin
g
p
o
in
t
in
th
e
s
ea
r
c
h
s
p
ac
e
m
a
k
in
g
it
s
u
itab
le
f
o
r
r
o
b
o
ts
h
a
v
in
g
o
n
b
o
a
r
d
s
en
s
o
r
.
T
h
e
al
g
o
r
ith
m
ca
n
p
r
o
v
i
d
e
o
p
tim
al
an
d
ef
f
icien
t
p
ath
as
well
a
s
m
an
ag
in
g
th
e
f
u
ll
s
p
ec
tr
u
m
o
f
m
ap
in
f
o
r
m
atio
n
,
f
r
o
m
co
m
p
lete
an
d
ac
cu
r
ate
m
ap
in
f
o
r
m
atio
n
t
o
l
ittl
e
o
r
n
o
m
ap
in
f
o
r
m
atio
n
.
[
2
3
]
.
I
n
[
2
4
]
D*
f
o
cu
s
ed
was
p
r
o
p
o
s
ed
with
th
e
g
o
al
o
f
im
p
r
o
v
in
g
th
e
c
h
ar
a
cter
is
tics
o
f
D*
.
T
h
is
alg
o
r
it
h
m
im
p
r
o
v
ed
th
e
ex
p
a
n
s
io
n
b
y
m
in
im
izin
g
th
e
n
u
m
b
er
o
f
n
o
d
es
th
at
n
ee
d
ed
to
b
e
an
aly
ze
d
as
well
as
th
e
co
m
p
u
tin
g
tim
e.
Au
th
o
r
in
[
2
5
]
p
r
esen
ted
a
3
D
g
r
id
D*
alg
o
r
ith
m
in
wh
ich
d
em
o
n
s
tr
ated
th
at
th
e
ch
an
g
e
co
u
ld
d
eliv
er
r
ea
l
-
tim
e
p
er
f
o
r
m
a
n
ce
at
a
lo
we
r
co
s
t.
A
m
o
d
if
ied
ap
p
r
o
ac
h
o
f
D*
f
o
r
ter
r
ain
-
b
ased
p
ath
p
la
n
n
in
g
is
p
r
o
p
o
s
ed
in
[
2
6
]
.
B
esid
es
th
e
d
is
tan
ce
to
b
e
tr
av
elled
,
th
e
ter
r
ain
s
lo
p
e
esti
m
ate
is
also
co
n
s
id
er
ed
in
th
e
co
s
t f
u
n
ctio
n
co
m
p
u
tatio
n
t
o
p
lan
th
e
p
ath
.
W
h
en
co
m
p
ar
ed
to
th
e
D*
m
eth
o
d
,
th
e
m
o
d
if
ied
D*
alg
o
r
ith
m
g
en
er
ates
m
o
r
e
ef
f
icien
t
r
esu
lts
s
in
ce
it
is
ab
le
to
av
o
id
p
ea
k
s
an
d
s
o
r
ea
ch
th
e
e
n
d
d
esti
n
atio
n
in
m
o
r
e
ef
f
icien
t w
ay
.
3.
SAM
P
L
I
NG
B
AS
E
D
P
AT
H
P
L
ANNI
NG
S
a
m
p
l
i
n
g
-
b
a
s
e
d
a
p
p
r
o
a
c
h
es
a
r
e
i
m
p
l
e
m
e
n
t
e
d
t
h
r
o
u
g
h
o
u
t
t
h
e
s
e
a
r
c
h
w
it
h
i
n
c
o
n
f
i
g
u
r
a
t
i
o
n
s
p
a
c
e
w
h
e
r
e
i
n
f
o
r
m
a
t
i
o
n
i
s
a
c
q
u
i
r
e
d
f
r
o
m
a
c
o
l
l
i
s
i
o
n
d
e
t
e
c
t
o
r
.
T
h
e
p
a
t
h
d
e
p
e
n
d
s
o
n
t
h
e
p
o
s
s
i
b
l
e
c
o
n
f
ig
u
r
a
t
i
o
n
a
n
d
c
h
e
c
k
s
c
o
l
l
is
i
o
n
s
o
t
h
a
t
t
h
e
c
o
n
f
i
g
u
r
a
t
i
o
n
'
s
v
a
li
d
i
t
y
c
a
n
b
e
v
e
r
i
f
i
ed
a
n
d
p
r
o
d
u
c
e
r
e
s
u
l
t
s
t
h
at
m
a
t
c
h
w
i
t
h
t
h
e
t
a
r
g
et
c
o
n
f
i
g
u
r
a
t
i
o
n
.
W
h
i
l
e
t
h
is
r
a
n
d
o
m
a
p
p
r
o
a
c
h
h
a
s
a
d
v
a
n
t
a
g
e
s
i
n
p
r
o
v
i
d
i
n
g
q
u
i
c
k
r
e
s
u
l
t
t
o
d
i
f
f
i
c
u
l
t
p
r
o
b
l
e
m
s
[
2
7
]
,
t
h
e
a
l
g
o
r
i
t
h
m
l
a
c
k
s
i
n
f
o
r
m
a
ti
o
n
o
n
t
h
e
e
x
is
t
e
n
ce
o
f
t
h
e
o
b
j
e
ct
i
n
t
h
e
c
o
n
f
i
g
u
r
a
t
i
o
n
s
p
a
c
e
s
i
n
ce
c
o
l
li
s
i
o
n
t
es
t
i
n
g
i
s
o
n
l
y
p
e
r
f
o
r
m
e
d
w
h
e
n
n
e
c
e
s
s
a
r
y
[
2
8
]
.
T
h
e
i
m
p
r
o
v
e
m
e
n
t,
d
e
s
c
r
i
p
ti
o
n
,
a
p
p
l
i
c
at
i
o
n
,
a
n
d
i
m
p
r
o
v
e
m
e
n
t
o
f
s
a
m
p
l
i
n
g
-
b
a
s
e
d
a
l
g
o
r
i
t
h
m
a
r
e
b
e
i
n
g
r
e
v
i
e
w
e
d
t
h
o
r
o
u
g
h
l
y
i
n
[
2
7
]
.
A
c
c
o
r
d
i
n
g
t
o
[
6
]
,
[
2
9
]
,
I
n
a
c
o
m
p
l
e
x
a
n
d
r
e
a
l
i
s
t
i
c
s
et
t
i
n
g
,
s
a
m
p
l
i
n
g
-
b
a
s
ed
a
l
g
o
r
i
t
h
m
s
a
r
e
m
o
r
e
p
r
o
m
i
s
in
g
c
o
m
p
a
r
e
d
t
o
g
r
a
p
h
-
b
a
s
e
d
a
lg
o
r
i
t
h
m
s
b
e
c
a
u
s
e
it
i
s
s
i
m
p
l
e
r
i
n
a
s
p
e
ct
s
o
f
r
e
p
r
e
s
e
n
t
at
i
o
n
a
n
d
c
o
m
p
u
t
a
t
i
o
n
.
I
n
s
a
m
p
l
i
n
g
-
b
a
s
e
d
p
a
t
h
p
l
a
n
n
i
n
g
,
t
h
e
r
e
a
r
e
t
w
o
c
o
m
m
o
n
m
e
t
h
o
d
s
;
r
a
p
i
d
l
y
e
x
p
lo
r
i
n
g
r
a
n
d
o
m
t
r
e
e
(
R
R
T
)
a
n
d
p
r
o
b
a
b
i
l
i
s
ti
c
r
o
a
d
m
a
p
(
PR
M
)
.
3
.
1
.
Ra
pid
ly
-
ex
plo
ring
r
a
nd
o
m
t
re
e
(
RRT)
R
R
T
is
a
tr
ee
-
g
r
o
win
g
alg
o
r
it
h
m
th
at
g
r
o
ws
a
tr
ee
f
r
o
m
th
e
in
itial
p
o
in
t
to
th
e
tar
g
et
p
o
i
n
t,
o
r
v
ice
v
er
s
a.
A
p
o
in
t
is
c
h
o
s
en
at
r
a
n
d
o
m
f
r
o
m
th
e
co
n
f
ig
u
r
atio
n
s
p
ac
e,
an
d
if
it
is
in
f
r
ee
s
p
ac
e,
a
c
o
n
n
ec
tio
n
to
th
e
clo
s
est
v
er
tex
in
t
h
e
tr
ee
is
att
em
p
ted
,
r
esu
ltin
g
in
a
r
a
p
id
ly
ex
p
lo
r
in
g
r
a
n
d
o
m
tr
ee
[
2
7
]
,
as
s
h
o
wn
in
Fig
u
r
e
2.
R
R
T
h
av
e
b
ee
n
p
r
o
v
ed
to
b
e
e
f
f
ec
tiv
e
at
a
d
d
r
ess
in
g
c
o
m
p
lic
ated
p
ath
-
p
lan
n
in
g
p
r
o
b
lem
s
in
h
ig
h
d
im
e
n
s
io
n
al
en
v
ir
o
n
m
en
ts
.
I
n
ad
d
itio
n
,
R
R
T
is
co
n
ce
p
tu
ally
s
im
p
le
an
d
ab
le
to
attain
p
r
o
b
a
b
ilis
tic
co
m
p
leten
ess
[
2
8
]
.
On
th
e
o
th
er
h
an
d
,
th
e
tr
ad
itio
n
al
R
R
T
ar
e
n
o
t
g
u
ar
an
teed
to
a
ch
iev
e
o
p
tim
ality
o
r
ev
e
n
p
r
o
d
u
cin
g
h
i
g
h
-
q
u
ality
p
ath
.
I
n
[
3
0
]
,
I
t
h
as
b
ee
n
s
h
o
wn
th
at
u
n
d
er
m
o
d
e
r
ate
s
p
ac
e
co
n
d
itio
n
s
,
th
e
c
o
s
t
o
f
t
h
e
b
e
s
t
r
o
u
te
in
th
e
R
R
T
ap
p
r
o
ac
h
es
a
n
o
n
-
o
p
tim
al
v
alu
e,
th
u
s
th
e
alg
o
r
ith
m
h
as
b
e
en
m
o
d
if
ied
t
o
o
b
tain
o
p
tim
al
r
esu
lt.
R
esear
ch
er
s
in
[
3
1
]
h
as
im
p
r
o
v
ed
th
e
alg
o
r
ith
m
to
ac
h
iev
e
o
p
tim
al
p
a
th
p
lan
n
in
g
in
a
co
s
t
s
p
ac
e
wh
ile
d
escr
ib
es
an
an
y
tim
e
m
o
tio
n
al
g
o
r
ith
m
th
a
t
b
ased
o
n
th
e
R
R
T
∗
wh
ich
a
b
le
to
q
u
ick
ly
d
is
co
v
er
an
in
it
ial
p
o
s
s
ib
le
s
o
lu
tio
n
an
d
th
en
c
o
n
v
er
g
es to
an
o
p
ti
m
al
s
o
lu
tio
n
.
I
m
p
r
o
v
e
d
alg
o
r
it
h
m
in
[
3
2
]
s
u
itab
le
f
o
r
h
an
d
lin
g
th
e
p
ath
p
la
n
n
in
g
with
th
r
ea
t
r
e
g
io
n
an
d
d
y
n
am
i
c
co
n
s
tr
ain
t.
T
h
e
o
ld
R
R
T
alg
o
r
ith
m
was
m
o
d
if
ied
in
th
is
a
p
p
r
o
ac
h
b
y
d
eletin
g
u
n
n
ec
ess
ar
y
n
o
d
es
an
d
co
n
s
tr
u
ctin
g
a
tr
an
s
itio
n
tr
ajec
t
o
r
y
,
w
h
ich
in
cr
ea
s
ed
th
e
UAV
'
s
s
af
ety
an
d
m
an
eu
v
er
a
b
ilit
y
.
F
ig
u
r
e
2
.
Pro
ce
d
u
r
e
o
f
e
x
ten
d
i
n
g
R
R
T
[
2
7
]
3
.
2
.
P
ro
ba
bil
is
t
ic
ro
a
dm
a
p
m
et
ho
d
(
RP
M
)
PR
M
co
n
s
is
t
o
f
s
ev
er
al
s
tep
s
.
T
h
e
f
ir
s
t
s
tep
is
co
n
f
ig
u
r
atio
n
.
B
y
s
elec
tin
g
co
o
r
d
in
ates
at
r
an
d
o
m
,
co
n
f
ig
u
r
atio
n
s
ar
e
s
am
p
led
.
T
h
en
,
in
p
h
ase
two
,
th
e
s
am
p
le
d
co
n
f
ig
u
r
atio
n
s
ar
e
ch
ec
k
e
d
f
o
r
co
llis
io
n
to
av
o
id
o
b
s
tacle
s
.
T
h
e
co
llis
io
n
-
f
r
ee
f
o
r
s
tar
t
an
d
tar
g
et
c
o
n
f
ig
u
r
ati
o
n
s
ar
e
k
e
p
t
as
m
iles
to
n
es,
an
d
ea
ch
m
iles
to
n
e
is
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
5
2
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci,
Vo
l.
24
,
No
.
2
,
No
v
em
b
er
2
0
2
1
:
1
0
1
7
-
1
0
2
6
1020
co
n
n
ec
ted
to
its
clo
s
est
n
eig
h
b
o
r
s
b
y
s
tr
aig
h
t
p
ath
s
.
Fro
m
th
e
lin
k
ed
m
iles
to
n
e,
th
e
c
o
llis
io
n
-
f
r
ee
lin
k
s
ar
e
r
etain
ed
as lo
ca
l p
ath
s
as n
ew
co
n
f
ig
u
r
atio
n
to
f
o
r
m
th
e
PR
M.
T
h
is
is
s
h
o
wn
in
Fig
u
r
e
3.
PR
M
g
en
er
ally
ap
p
lied
in
lar
g
e
an
d
co
m
p
le
x
co
n
f
i
g
u
r
atio
n
s
p
ac
e
I
t h
as b
ee
n
s
h
o
wn
in
[
3
3
]
,
[
3
4
]
th
a
t
PR
M
i
s
p
r
o
b
ab
ilis
tically
co
m
p
lete.
B
ased
o
n
[
3
5
]
,
th
e
r
ate
o
f
co
n
v
er
g
e
n
ce
o
f
PR
M
o
n
th
e
o
th
er
h
an
d
,
is
s
lo
w,
an
d
p
ath
s
g
e
n
er
ated
ar
e
n
o
t
o
p
tim
al.
Acc
o
r
d
i
n
g
to
[
2
9
]
th
e
s
o
lu
tio
n
s
f
r
o
m
PR
M
ca
n
lea
d
th
e
d
ev
ice
to
f
ail
an
d
m
o
v
e
v
er
y
clo
s
e
to
th
e
o
b
s
tacle
s
in
th
e
co
n
f
ig
u
r
atio
n
,
m
ak
in
g
it
im
p
r
ac
tical.
Var
io
u
s
m
o
d
if
ica
tio
n
h
as
b
ee
n
d
o
n
e
to
th
e
o
r
i
g
in
al
PR
M
f
o
r
b
etter
r
esu
lt.
T
h
e
r
esu
ltin
g
r
o
ad
m
a
p
f
r
o
m
m
o
d
if
icatio
n
m
ad
e
in
[
2
9
]
ca
n
th
en
b
e
ap
p
lied
to
p
r
o
d
u
ce
m
u
ch
p
r
ac
tical
p
ath
s
.
I
n
[
3
6
]
w
ith
s
o
m
e
im
p
r
o
v
em
en
t
o
f
th
e
p
ath
ef
f
icien
cy
an
d
co
s
t,
R
PM
h
as
b
ee
n
im
p
lem
e
n
ted
to
s
o
lv
e
p
at
h
p
la
n
n
in
g
p
r
o
b
lem
s
u
n
d
er
u
n
k
n
o
wn
en
v
ir
o
n
m
en
t.
I
n
[
3
7
]
,
t
h
e
au
th
o
r
p
r
o
p
o
s
es a
m
eth
o
d
f
o
r
q
u
ad
r
o
to
r
UAVs to
f
ly
in
f
o
r
m
atio
n
with
co
llis
io
n
av
o
i
d
an
ce
u
s
in
g
PR
M.
Fig
u
r
e
3
.
PR
M
p
r
o
ce
s
s
4.
B
I
O
L
O
G
I
CAL
L
Y
I
NSPI
RE
D
P
AT
H
P
L
ANN
I
NG
B
io
lo
g
ically
in
s
p
ir
ed
p
ath
p
l
an
n
in
g
is
o
n
e
o
f
th
e
m
aj
o
r
s
u
b
s
ets
o
f
n
at
u
r
al
c
o
m
p
u
ta
tio
n
.
I
t
is
d
escr
ib
ed
as
th
e
co
m
b
in
atio
n
o
f
co
n
n
ec
tio
n
is
m
,
s
o
cial
b
eh
av
io
r
,
an
d
em
e
r
g
en
ce
.
W
ith
th
e
u
s
e
o
f
co
m
p
u
ter
s
,
th
is
m
eth
o
d
is
b
ein
g
im
p
lem
en
ted
to
m
o
d
el
liv
in
g
p
h
en
o
m
e
n
o
n
,
an
d
at
th
e
s
am
e
tim
e
it
a
ttem
p
ts
to
en
h
an
ce
th
e
u
s
e
o
f
co
m
p
u
ter
f
o
r
a
b
ette
r
f
u
tu
r
e.
Natu
r
al
i
n
s
p
ir
ed
p
ath
p
lan
n
in
g
alg
o
r
ith
m
ca
n
b
e
ca
t
eg
o
r
ized
i
n
to
th
r
ee
m
eth
o
d
s
[
3
8
]
,
wh
ich
ev
o
lu
tio
n
ar
y
,
s
war
m
in
tellig
en
ce
a
n
d
n
eu
r
o
d
y
n
am
ic.
I
n
th
is
p
a
p
er
,
alg
o
r
ith
m
s
f
r
o
m
two
ty
p
es
o
f
m
eth
o
d
ar
e
b
ein
g
d
is
cu
s
s
ed
wh
ich
g
en
etic
al
g
o
r
ith
m
u
s
in
g
ev
o
lu
tio
n
ar
y
m
et
h
o
d
,
also
p
a
r
ticle
s
war
m
o
p
tim
izatio
n
(
PSO
)
an
d
AC
O
wh
ich
u
s
ed
s
war
m
in
tellig
en
ce
m
eth
o
d
.
R
esear
ch
ar
ticle
[
3
9
]
,
[
4
0
]
d
is
cu
s
s
th
o
r
o
u
g
h
ly
o
n
th
e
s
wa
r
m
o
p
ti
m
izatio
n
tech
n
iq
u
e
s
u
ch
as PSO,
AC
O
an
d
o
th
er
s
.
4
.
1
.
G
enet
ic
a
lg
o
ri
t
hm
(
G
A
)
A
g
en
etic
al
g
o
r
ith
m
(
GA)
r
es
id
es
to
m
eta
-
h
eu
r
is
tic
s
ea
r
ch
alg
o
r
ith
m
s
[
4
]
,
[
7
]
th
at
is
m
o
tiv
ated
b
y
th
e
p
r
in
cip
le
o
f
n
atu
r
al
ev
o
l
u
tio
n
o
f
C
h
ar
les
Dar
win
.
GA
is
b
ased
o
n
n
atu
r
al
g
e
n
etics,
wh
ich
b
en
ef
its
f
r
o
m
p
r
o
ce
s
s
es
s
u
ch
as
n
atu
r
al
s
elec
tio
n
,
cr
o
s
s
o
v
er
,
a
n
d
m
u
tatio
n
[
4
1
]
.
T
h
is
m
eth
o
d
u
s
ed
t
h
e
n
atu
r
al
s
elec
tio
n
s
y
s
tem
in
wh
ich
th
e
m
o
s
t
s
u
it
ab
le
in
d
iv
i
d
u
als
ar
e
ch
o
s
en
f
o
r
r
ep
r
o
d
u
ctio
n
to
c
r
ea
te
n
ex
t
-
g
en
er
atio
n
o
f
f
s
p
r
in
g
.
T
h
is
b
io
lo
g
ical
ev
o
lu
tio
n
ca
n
b
e
ap
p
lied
to
s
o
lv
e
b
o
th
co
n
s
t
r
ain
ed
an
d
u
n
co
n
s
tr
ain
ed
o
p
ti
m
izatio
n
p
r
o
b
lem
s
.
GA
ca
n
r
ap
id
ly
o
b
tain
an
y
s
o
lu
tio
n
b
u
t
it
co
u
ld
r
esu
lt
in
lo
ca
l
o
p
ti
m
u
m
s
o
lu
tio
n
if
th
e
alg
o
r
ith
m
o
p
er
ates
in
an
im
p
r
o
p
e
r
ly
-
d
e
f
in
ed
f
itn
ess
f
u
n
ctio
n
[
3
8
]
as
th
e
co
n
v
e
r
g
en
ce
s
p
ee
d
will
r
ed
u
ce
wh
en
it
ap
p
r
o
ac
h
es
th
e
o
p
tim
al
s
o
lu
tio
n
[
3
]
,
th
u
s
m
a
k
in
g
GA
co
m
p
u
tatio
n
ally
ex
p
e
n
s
iv
e
an
d
p
r
ac
tically
in
c
o
m
p
l
ete
[
2
8
]
.
Alg
o
r
it
h
m
im
p
r
o
v
em
e
n
t
in
[
4
2
]
s
h
o
ws
th
at
th
eir
m
eth
o
d
ca
n
b
o
o
s
t
th
e
g
lo
b
al
s
ea
r
ch
ab
ilit
y
o
f
g
en
eti
c
alg
o
r
ith
m
,
as
well
as
im
p
r
o
v
in
g
t
h
e
q
u
ality
an
d
ac
cu
r
ac
y
o
f
UAV
f
lig
h
t
p
ath
.
GA
also
b
ein
g
co
m
b
in
e
d
with
PR
M
in
[
4
3
]
to
s
o
lv
e
m
o
b
ile
r
o
b
o
t
p
ath
b
ec
a
u
s
e
as
co
m
p
ar
ed
to
o
th
er
m
e
th
o
d
s
,
GA
h
as
th
e
p
o
ten
tial
to
lo
o
k
f
o
r
o
p
tim
al
s
o
lu
tio
n
s
in
a
la
r
g
er
s
ea
r
ch
s
p
ac
e.
I
n
[
4
4
]
,
th
e
c
r
o
s
s
o
v
er
,
s
elec
tio
n
an
d
m
u
tatio
n
o
f
G
A
h
elp
s
to
im
p
r
o
v
e
e
n
e
r
g
y
o
p
t
i
m
i
z
a
ti
o
n
p
a
t
h
p
l
an
n
i
n
g
f
o
r
n
e
a
r
o
p
t
i
m
a
l
o
r
o
p
t
i
m
a
l
s
o
l
u
ti
o
n
.
T
o
d
e
a
l
w
it
h
s
c
e
n
a
r
i
o
s
i
n
v
o
l
v
i
n
g
o
b
s
t
a
c
l
es
a
n
d
b
u
i
l
d
i
n
g
s
,
a
c
o
v
e
r
a
g
e
p
a
t
h
p
l
a
n
n
i
n
g
a
p
p
r
o
a
c
h
b
a
s
e
d
o
n
3
D
s
t
r
u
c
t
u
r
e
m
a
p
p
i
n
g
i
s
p
r
o
p
o
s
e
d
i
n
[
4
5
]
.
T
h
e
co
v
er
a
g
e
p
ath
is
ca
lcu
lated
u
s
in
g
a
GA,
an
d
o
n
ly
th
e
f
r
ee
s
p
ac
es
an
d
ar
ea
s
with
tar
g
et
b
elo
w
th
e
h
eig
h
t
f
ly
in
g
ar
e
co
n
s
id
er
e
d
.
4
.
2
.
P
a
rt
icle
s
wa
rm
o
ptim
iz
a
t
io
n
(
P
SO
)
Par
ticle
s
war
m
o
p
tim
izatio
n
(
PS
O)
is
a
tr
ad
itio
n
al
m
eta
-
h
e
u
r
is
tic
p
r
ac
tically
u
s
ed
to
ad
d
r
ess
g
lo
b
al
o
p
tim
izatio
n
is
s
u
es
u
s
in
g
th
e
s
war
m
in
g
ch
a
r
ac
ter
is
tic
o
f
b
io
lo
g
ical
p
o
p
u
latio
n
s
.
I
t
was
cr
ea
ted
in
t
h
e
m
id
-
1
9
9
0
s
as
a
m
ea
n
s
o
f
r
ec
r
ea
tin
g
th
e
well
-
ch
o
r
eo
g
r
ap
h
ed
m
o
t
io
n
o
f
a
f
lo
c
k
o
f
b
ir
d
s
[
4
6
]
.
F
ig
u
r
e
4
s
h
o
ws
h
o
w
ea
ch
p
ar
ticle
f
in
d
its
n
ex
t
lo
ca
tio
n
to
war
d
s
th
e
tar
g
et.
E
ac
h
p
ar
ticle
in
th
e
alg
o
r
ith
m
c
h
an
g
es
its
co
n
d
itio
n
s
to
f
in
d
tar
g
et
b
ased
o
n
v
elo
city
v
alu
e.
As
s
h
o
wn
i
n
(
1
)
v
elo
city
v
alu
e
i
n
d
icate
s
h
o
w
m
u
ch
d
is
tan
ce
,
p
o
s
itio
n
an
d
s
p
ee
d
o
f
a
p
a
r
ticle
ca
n
b
e
m
o
d
if
ied
an
d
it
is
a
f
f
ec
ted
b
y
th
r
ee
f
ac
to
r
s
;
its
o
wn
in
er
ti
a,
p
ar
ticle
m
em
o
r
y
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2502
-
4
7
5
2
R
ev
iew
o
n
p
a
th
p
la
n
n
in
g
a
lg
o
r
ith
m
fo
r
u
n
ma
n
n
ed
a
eria
l v
eh
icles
(
N
u
r
u
l S
a
lih
a
A
ma
n
i I
b
r
a
h
im
)
1021
in
f
lu
en
ce
th
at
p
u
lls
p
a
r
ticle
to
war
d
p
e
r
s
o
n
al
b
est
(
p
B
est),
an
d
s
war
m
in
f
lu
en
ce
th
at
p
u
lls
p
ar
ticles
to
war
d
s
s
war
m
b
est
(
g
B
est)
[
4
0
]
.
A
p
B
est
v
alu
e
s
p
ec
if
ies
h
o
w
clo
s
e
th
e
p
a
r
ticle'
s
d
ata
h
as
ev
er
co
m
e
to
th
e
tar
g
et.
W
h
en
th
e
n
eig
h
b
o
r
h
o
o
d
o
f
a
p
ar
ticle
f
o
r
m
s
a
s
war
m
,
th
e
b
est p
o
s
itio
n
in
th
e
n
eig
h
b
o
r
h
o
o
d
,
g
B
est is
o
b
tain
ed
.
+
1
=
+
1
1
(
−
)
+
2
2
(
−
)
(
1
)
W
ith
th
e
b
alan
ce
th
e
p
B
est
a
n
d
g
B
est
o
p
er
atio
n
s
in
PS
O,
it
ea
s
es
in
th
e
p
r
o
ce
s
s
o
f
g
en
er
atin
g
an
o
p
tim
u
m
p
ath
[
4
]
,
[
2
8
]
.
Acc
o
r
d
in
g
to
[
4
7
]
,
b
ec
au
s
e
o
f
its
s
tr
aig
h
tf
o
r
war
d
im
p
lem
en
tatio
n
th
eo
r
y
an
d
ab
ilit
y
to
p
r
o
v
id
e
g
B
est
f
o
r
all
p
ar
ticles,
PS
O
is
well
s
u
ited
f
o
r
u
s
e
in
UAV
r
o
u
te
p
lan
n
in
g
an
d
o
th
er
o
p
tim
izatio
n
task
s
.
I
t
also
i
s
ea
s
y
to
b
e
im
p
lem
en
ted
[
3
8
]
,
h
ig
h
p
r
ec
is
io
n
an
d
f
ast
co
n
v
er
g
en
ce
[
3
]
,
b
u
t
if
th
e
en
v
ir
o
n
m
e
n
t
b
ec
o
m
e
co
m
p
licated
,
it
ca
n
lead
co
n
v
e
r
g
en
ce
s
p
ee
d
is
s
u
e
s
[
3
8
]
.
PS
O
is
ab
le
to
g
iv
e
p
ath
co
m
p
leten
ess
ac
co
r
d
in
g
to
r
ev
iew
in
[
2
8
]
.
I
n
[
4
8
]
,
a
n
ew
PS
O
-
b
as
ed
tech
n
iq
u
e
ca
lled
Ad
ap
ti
v
e
Par
ticle
Swar
m
Op
tim
izatio
n
is
d
ev
elo
p
ed
,
an
d
it
is
co
m
p
ar
ed
to
P
SO
in
ter
m
s
o
f
p
ath
len
g
th
an
d
tim
e
in
s
tatic
s
e
ttin
g
s
,
an
d
it
s
u
cc
ess
f
u
lly
av
o
id
s
o
b
s
tacle
s
an
d
r
ea
ch
es
th
e
g
o
al
in
less
tim
e
th
an
tr
ad
itio
n
al
PS
O.
T
h
e
co
m
p
r
e
h
en
s
iv
ely
im
p
r
o
v
e
d
PS
O
p
r
o
p
o
s
ed
a
n
d
a
n
aly
ze
d
b
y
a
u
th
o
r
in
[
4
7
]
ca
p
a
b
le
o
f
p
r
o
d
u
cin
g
f
aster
co
n
v
er
g
en
ce
an
d
o
p
tim
al
s
o
lu
tio
n
.
Fig
u
r
e
4
.
Par
ticle
s
war
m
o
p
tim
izatio
n
p
r
o
ce
s
s
4
.
3
.
Ant
c
o
lo
ny
o
pti
m
iza
t
io
n
(
ACO
)
Ma
r
co
Do
r
ig
o
in
tr
o
d
u
ce
d
a
n
t
co
lo
n
y
o
p
tim
izatio
n
(
AC
O)
in
1
9
9
2
.
AC
O,
lik
e
PS
O,
i
s
a
m
eta
-
h
eu
r
is
tic
an
d
p
r
o
b
a
b
ilis
tic
tec
h
n
iq
u
e
f
o
c
u
s
ed
o
n
an
t
co
lo
n
y
ac
tiv
ity
in
s
ea
r
ch
in
g
f
o
r
f
o
o
d
an
d
f
o
r
m
in
g
p
ath
s
af
ter
f
in
d
in
g
its
s
o
u
r
ce
.
A
n
t,
p
h
er
o
m
o
n
e,
d
ae
m
o
n
ac
tio
n
,
a
n
d
d
ec
e
n
tr
alize
d
c
o
n
tr
o
l
ar
e
f
o
u
r
k
ey
co
m
p
o
n
en
ts
o
f
AC
O
[
3
9
]
.
An
ts
r
elea
s
e
p
h
er
o
m
o
n
es
as
th
ey
m
o
v
e
th
r
o
u
g
h
t
h
e
s
ea
r
ch
ar
ea
,
an
d
th
e
q
u
an
tity
o
f
th
ese
p
h
er
o
m
o
n
es
in
d
icate
s
th
e
tr
ai
l's
in
ten
s
ity
.
Dae
m
o
n
ac
ts
ar
e
u
s
ed
to
co
llect
g
lo
b
al
i
n
f
o
r
m
atio
n
to
d
ec
id
e
if
ad
d
itio
n
al
p
h
er
o
m
o
n
es
n
ee
d
t
o
b
e
in
tr
o
d
u
ce
d
in
o
r
d
er
to
p
r
o
m
o
te
c
o
n
v
e
r
g
en
ce
.
Dec
en
tr
al
ized
co
n
t
r
o
l
is
t
h
en
im
p
lem
en
ted
to
m
a
k
e
it
m
o
r
e
r
o
b
u
s
t
an
d
f
le
x
ib
le
in
a
co
m
p
lex
s
ettin
g
.
Fig
u
r
e
5
s
h
o
ws
an
t
co
lo
n
y
o
p
tim
izatio
n
Alg
o
r
ith
m
p
r
o
ce
s
s
es
wh
er
e
ea
r
ly
p
r
o
ce
s
s
,
th
e
an
ts
s
tar
t
f
in
d
a
p
ath
b
etwe
en
n
est
an
d
f
o
o
d
a
n
d
lay
p
h
er
o
m
o
n
e.
T
h
e
an
ts
th
en
wen
t
th
r
o
u
g
h
all
p
o
s
s
ib
le
p
ath
s
an
d
last
ly
m
ajo
r
ity
o
f
th
em
o
p
tin
g
f
o
r
th
e
o
n
e
with
th
e
h
ig
h
est p
h
e
r
o
m
o
n
e.
Fig
u
r
e
5
.
An
t
c
o
lo
n
y
o
p
tim
iza
tio
n
alg
o
r
ith
m
p
r
o
ce
s
s
es
[
3
9
]
Pro
b
lem
s
with
s
p
ec
if
ic
a
n
d
clea
r
ly
p
r
e
d
ef
in
e
d
s
o
u
r
ce
an
d
d
esti
n
atio
n
ar
e
t
h
e
m
o
s
t
f
i
t
f
o
r
AC
O
im
p
lem
en
tatio
n
[
4
1
]
an
d
m
o
r
e
ap
p
licab
le
f
o
r
p
r
o
b
lem
s
th
at
r
eq
u
ir
es
cr
is
p
s
r
esu
lts
.
T
h
e
b
en
ef
its
o
f
AC
O
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
5
2
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci,
Vo
l.
24
,
No
.
2
,
No
v
em
b
er
2
0
2
1
:
1
0
1
7
-
1
0
2
6
1022
in
clu
d
e
r
ap
i
d
ex
p
l
o
r
atio
n
o
f
g
o
o
d
s
o
l
u
tio
n
f
in
d
in
g
a
n
d
d
i
s
tr
ib
u
ted
co
m
p
u
tin
g
,
wh
ic
h
p
r
ev
en
ts
p
r
e
m
atu
r
e
co
n
v
er
g
en
ce
[
3
9
]
.
AC
O
ca
n
ad
ap
t
to
n
ew
c
h
an
g
es
m
ak
in
g
it
s
u
itab
le
to
b
e
im
p
le
m
en
ted
in
d
y
n
am
ic
ap
p
licatio
n
s
.
AC
O,
h
as
d
is
ad
v
an
tag
es,
s
u
ch
as
s
lo
w
co
n
v
e
r
g
en
ce
s
p
ee
d
as
co
m
p
ar
ed
to
o
th
er
m
etah
eu
r
is
tic
ap
p
r
o
ac
h
es.
T
h
e
co
n
v
er
g
e
n
c
e
is
g
u
ar
a
n
teed
,
b
u
t
as
t
h
e
co
m
p
lex
ity
o
f
th
e
s
ea
r
ch
s
p
ac
e
in
cr
ea
s
es,
th
e
co
n
v
er
g
en
ce
tim
e
b
ec
o
m
es
u
n
ce
r
tain
[
3
8
]
,
[
4
0
]
.
As
in
[
1
0
]
,
th
e
co
m
p
ar
is
o
n
r
esu
lt
p
r
o
v
e
s
th
at
AC
O
h
as
th
e
lo
n
g
est
s
im
u
latio
n
r
u
n
tim
e.
An
t
co
lo
n
y
o
p
tim
izatio
n
is
b
e
in
g
en
h
a
n
ce
d
i
n
[
4
9
]
.
E
x
p
er
i
m
en
tal
r
esu
lt
o
f
t
h
e
im
p
lem
en
tatio
n
in
c
o
m
p
lex
s
itu
atio
n
with
d
en
s
e
o
b
s
tacle
s
s
h
o
ws
th
at
t
h
e
e
n
h
an
ce
d
al
g
o
r
ith
m
is
ab
le
to
p
r
o
v
id
e
s
atis
f
ac
to
r
y
p
ath
p
lan
n
in
g
a
n
d
m
ee
t
t
h
e
c
o
m
p
u
tatio
n
al
tim
e
r
eq
u
ir
e
m
en
t.
R
esear
c
h
in
[
5
0
]
s
h
o
ws
th
at
in
b
o
th
s
im
p
le
an
d
co
m
p
le
x
en
v
ir
o
n
m
en
ts
,
AC
O
ca
n
d
is
co
v
er
a
n
ea
r
o
p
tim
al
p
ath
an
d
av
o
id
o
b
s
tacle
s
in
a
tim
ely
m
an
n
er
.
T
h
e
n
ew
d
y
n
a
m
ic
alg
o
r
ith
m
p
r
o
p
o
s
ed
in
[
5
1
]
in
co
r
p
o
r
ates
AC
O
wi
th
p
o
ten
tial
f
ield
.
I
t
u
s
es
an
ar
tific
ial
f
ield
to
s
im
u
late
th
e
en
v
ir
o
n
m
en
t
f
o
r
c
o
llis
io
n
-
f
r
ee
p
ath
p
lan
n
in
g
f
o
r
th
e
UAV
wh
ile
tak
in
g
in
to
ac
co
u
n
t
o
n
b
o
th
s
tatic
an
d
d
y
n
am
ic
th
r
ea
ts
.
I
n
[
5
2
]
,
p
at
h
p
l
an
n
in
g
i
n
teg
r
ates
im
m
u
n
e
n
et
wo
r
k
o
p
tim
izatio
n
with
an
t c
o
lo
n
y
o
p
tim
izatio
n
to
im
p
r
o
v
e
th
e
a
b
ilit
y
o
f
a
m
u
lt
i U
AV
s
y
s
tem
to
f
in
d
th
e
s
h
o
r
test
p
ath
.
5.
SUM
M
ARY
O
N
P
A
T
H
P
L
ANNING
T
RAI
T
S
T
ab
le
1
s
u
m
m
ar
izes
th
e
o
b
s
er
v
atio
n
o
f
tr
aits
in
te
r
m
s
o
f
o
p
tim
al
p
ath
,
p
r
o
b
ab
ilis
tic
co
m
p
leten
ess
,
an
d
ap
p
licatio
n
a
r
ea
s
f
o
r
ea
ch
alg
o
r
ith
m
.
T
h
r
ee
g
r
a
p
h
s
ea
r
ch
alg
o
r
ith
m
s
d
is
cu
s
s
ed
in
th
is
p
ap
er
,
all
ap
p
licab
le
in
f
in
d
in
g
s
h
o
r
test
p
ath
p
la
n
n
i
n
g
an
d
h
as
f
air
p
e
r
f
o
r
m
an
ce
a
n
d
ca
n
im
p
lem
en
ted
to
co
m
p
l
ete
o
n
lin
e
r
ea
l
-
tim
e
p
ath
p
lan
n
in
g
.
Dijk
s
tr
a’
s
alg
o
r
ith
m
ca
n
n
o
t
alwa
y
s
p
r
o
v
id
e
o
p
tim
al
p
ath
a
n
d
n
o
t
s
u
itab
le
f
o
r
v
ast
an
d
h
ig
h
d
im
en
s
io
n
al
ar
ea
in
ter
m
s
o
f
r
u
n
tim
e
as
its
d
ep
en
d
en
c
y
o
n
th
e
n
u
m
b
er
o
f
n
o
d
es.
A*
b
ein
g
in
tr
o
d
u
ce
d
to
ad
d
r
ess
th
e
s
h
o
r
tc
o
m
in
g
o
f
Dijk
s
tr
a’
s
,
m
ak
e
u
s
ed
t
h
e
ad
v
a
n
tag
es
o
f
h
eu
r
is
tic
m
eth
o
d
.
D
*
is
th
en
in
tr
o
d
u
ce
d
to
s
wif
tly
r
ep
air
s
o
lu
tio
n
s
wh
en
th
e
en
v
ir
o
n
m
en
t
ch
a
n
g
es.
Fo
r
s
am
p
lin
g
-
b
ased
m
eth
o
d
,
i
t
is
b
ein
g
f
o
u
n
d
th
at
b
o
th
alg
o
r
ith
m
s
,
R
R
T
an
d
P
R
M
ar
e
b
o
th
s
u
itab
le
in
s
o
l
v
in
g
co
m
p
le
x
p
ath
p
lan
n
in
g
p
r
o
b
lem
in
h
ig
h
-
d
im
en
s
io
n
al
s
p
ac
es.
T
h
ey
ar
e
ab
le
to
ac
h
iev
e
p
r
o
b
ab
ilis
tic
co
m
p
leten
ess
b
u
t
h
av
in
g
s
lo
w
co
m
p
u
tatio
n
tim
e
an
d
p
ath
p
r
o
d
u
ce
s
ar
e
n
o
t
o
p
tim
al.
I
n
b
io
lo
g
ically
in
s
p
ir
ed
alg
o
r
ith
m
,
GA
is
in
s
p
ir
ed
f
r
o
m
ev
o
lu
tio
n
ar
y
m
eth
o
d
wh
ile
PS
O,
an
d
AC
O
ar
e
in
s
p
ir
ed
b
y
s
war
m
.
GA
is
s
u
itab
le
to
b
e
ap
p
lied
in
f
in
d
in
g
s
o
lu
tio
n
s
in
wid
e
s
ea
r
ch
s
p
ac
e
an
d
r
ap
id
ly
o
b
ta
in
an
y
s
o
lu
tio
n
ef
f
icien
tly
.
P
SO
in
o
th
er
h
an
d
h
av
in
g
f
ast
co
n
v
er
g
e
n
ce
s
p
ee
d
b
u
t
it
b
ec
o
m
es
p
r
o
b
lem
as
th
e
en
v
ir
o
n
m
en
t
b
ec
o
m
es
c
o
m
p
licated
b
u
t
it
s
till
ab
le
to
p
r
o
v
id
e
p
ath
o
p
tim
ality
an
d
co
m
p
leten
ess
m
ak
in
g
it
f
it
f
o
r
p
r
o
b
lem
s
with
d
y
n
am
i
ca
lly
ch
an
g
in
g
la
n
d
s
ca
p
es
an
d
to
f
in
d
m
u
ltip
le
s
o
lu
tio
n
.
L
astl
y
,
AC
O
id
ea
l
f
o
r
p
r
o
b
lem
s
with
p
r
ed
ef
in
ed
s
o
u
r
ce
an
d
d
esti
n
atio
n
.
I
t
ab
le
to
g
iv
e
p
ath
co
m
p
leten
ess
wh
ich
s
u
itab
le
f
o
r
p
r
o
b
lem
s
th
at
r
e
q
u
ir
e
f
ir
m
an
d
clea
n
r
esu
lt.
T
ab
le
1
.
Path
p
la
n
n
in
g
alg
o
r
it
h
m
s
u
m
m
ar
y
O
p
t
i
mal
P
a
t
h
P
r
o
b
a
b
i
l
i
st
i
c
C
o
m
p
l
e
t
e
n
e
ss
A
p
p
l
i
c
a
t
i
o
n
I
mp
l
e
me
n
t
e
d
i
n
D
i
j
k
st
r
a
’
s
No
No
G
r
a
p
h
S
e
a
r
c
h
F
i
n
d
i
n
g
s
h
o
r
t
e
st
si
n
g
l
e
s
o
u
r
c
e
p
a
t
h
p
l
a
n
n
i
n
g
[
1
1
]
-
[
1
3
]
A*
Y
e
s
No
F
i
n
d
i
n
g
s
h
o
r
t
e
st
p
a
t
h
p
l
a
n
n
i
n
g
[
1
5
]
-
[
1
9
]
D*
Y
e
s
No
S
o
l
v
e
g
r
a
p
h
-
b
a
s
e
d
c
o
st
o
p
t
i
m
i
z
a
t
i
o
n
p
r
o
b
l
e
m
f
o
r
w
h
i
c
h
a
r
c
c
o
s
t
s
c
h
a
n
g
e
d
u
r
i
n
g
t
h
e
t
r
a
v
e
r
se
o
f
t
h
e
so
l
u
t
i
o
n
p
a
t
h
.
[
2
5
]
,
[
2
6
]
RRT
No
Y
e
s
S
a
mp
l
i
n
g
B
a
se
d
S
o
l
v
e
c
o
m
p
l
e
x
p
a
t
h
p
l
a
n
n
i
n
g
p
r
o
b
l
e
m
i
n
h
i
g
h
-
d
i
m
e
n
s
i
o
n
a
l
s
p
a
c
e
s
[
3
0
]
-
[
3
2
]
,
[
5
3
]
R
P
M
No
Y
e
s
S
o
l
v
e
c
o
m
p
l
e
x
p
a
t
h
p
l
a
n
n
i
n
g
p
r
o
b
l
e
m
i
n
h
i
g
h
-
d
i
m
e
n
s
i
o
n
a
l
s
p
a
c
e
s
[
2
9
]
,
[
3
6
]
,
[
3
7
]
GA
No
No
B
i
o
l
o
g
i
c
a
l
l
y
I
n
sp
i
r
e
d
S
e
a
r
c
h
f
o
r
o
p
t
i
mu
m
so
l
u
t
i
o
n
s
i
n
a
w
i
d
e
r
s
e
a
r
c
h
sp
a
c
e
[
4
2
]
-
[
4
5
]
,
[
5
4
]
PSO
Y
e
s
Y
e
s
S
o
l
v
e
p
r
o
b
l
e
ms
w
i
t
h
d
y
n
a
mi
c
a
l
l
y
c
h
a
n
g
i
n
g
l
a
n
d
s
c
a
p
e
s
,
a
n
d
t
o
f
i
n
d
m
u
l
t
i
p
l
e
so
l
u
t
i
o
n
s
[
4
7
]
,
[
4
8
]
,
[
5
5
]
,
[
5
6
]
A
C
O
No
Y
e
s
S
o
l
v
e
p
r
o
b
l
e
ms
w
i
t
h
p
r
e
d
e
f
i
n
e
d
so
u
r
c
e
a
n
d
d
e
s
t
i
n
a
t
i
o
n
.
A
p
p
l
i
c
a
b
l
e
f
o
r
p
r
o
b
l
e
ms
t
h
a
t
r
e
q
u
i
r
e
s
c
r
i
sp
s
r
e
s
u
l
t
s
[
4
9
]
-
[
5
2
]
,
[
5
7
]
5
.
1
.
P
SO
a
s
ef
f
icient
pa
t
h p
la
nn
ing
I
n
th
e
co
n
tex
t
o
f
ex
p
e
n
d
in
g
a
n
d
v
ast
n
etwo
r
k
o
f
i
n
ter
n
et
o
f
th
in
g
s
(
I
o
T
)
,
UAVs
ar
e
b
ei
n
g
u
tili
ze
d
t
o
co
llect
u
p
lin
k
d
ata
f
r
o
m
g
r
o
u
n
d
I
o
T
d
ev
ices
b
y
ac
ti
n
g
as
ae
r
ial
g
atew
ay
(
AG)
.
Usi
n
g
ae
r
ial
g
atew
ay
ca
n
r
ed
u
ce
th
e
en
er
g
y
u
s
ed
b
y
I
o
T
d
ev
ices
as
th
e
I
o
T
d
ev
ices
(
I
D)
ca
n
n
o
t
tr
an
s
m
it
d
ata
o
v
e
r
lo
n
g
d
is
tan
ce
,
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6
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RE
F
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NC
E
S
[1
]
T.
Ca
b
re
ira,
L
.
Briso
lara
,
a
n
d
P
.
R.
F
e
rre
ira,
“
S
u
rv
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y
o
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Co
v
e
ra
g
e
P
a
th
P
lan
n
in
g
with
Un
m
a
n
n
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d
Ae
rial
Ve
h
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s,”
Dr
o
n
e
s
,
v
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l.
3
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o
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1
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9
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[2
]
D.
G
o
n
z
á
lez
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P
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re
z
,
V.
M
il
a
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s
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a
n
d
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.
Na
sh
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"
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Re
v
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o
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Tec
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iq
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Zh
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W.
Li
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a
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Ch
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,
“
S
y
m
m
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try
P
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th
P
lan
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fo
r
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h
e
M
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t :
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[4
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O.
S
o
u
issi,
R.
Be
n
a
ti
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,
D
.
D
u
v
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v
ier,
A.
Arti
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a
,
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Be
la
n
g
e
r,
a
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P
.
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,
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:
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p
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1
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8
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[5
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M
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.
C
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F
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S
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[6
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L.
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Li
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1
7
,
1
9
9
4
.
[2
4
]
A.
S
ten
tz,
“
Th
e
F
o
c
u
ss
e
d
D
*
Alg
o
rit
h
m
f
o
r
Re
a
l
-
Ti
m
e
Re
p
la
n
n
i
n
g
,
”
Pro
c
e
e
d
in
g
s
o
f
1
4
th
In
t
e
rn
a
ti
o
n
a
l
J
o
i
n
t
Co
n
fer
e
n
c
e
o
n
Arti
fi
c
i
a
l
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n
telli
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n
c
e
,
1
9
9
5
,
p
p
.
1
6
5
2
-
1
6
5
9
.
[2
5
]
J.
Ca
rste
n
,
D.
F
e
r
g
u
so
n
,
a
n
d
A.
S
ten
tz,
"
3
D
F
ield
D:
Im
p
r
o
v
e
d
P
a
th
P
lan
n
in
g
a
n
d
Re
p
la
n
n
i
n
g
in
Th
re
e
Dim
e
n
sio
n
s,"
2
0
0
6
IE
EE
/R
S
J
In
t
e
rn
a
ti
o
n
a
l
Co
n
fer
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n
c
e
o
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I
n
telli
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n
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ms
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p
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o
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.
1
1
0
9
/IROS
.
2
0
0
6
.
2
8
2
5
1
6
.
[2
6
]
C.
S
a
ra
n
y
a
,
M
.
Un
n
i
k
rish
n
a
n
,
S
.
A.
Ali,
D.
S
.
S
h
e
e
la,
a
n
d
V
.
R.
Lalit
h
a
m
b
ik
a
,
“
Terra
in
Ba
se
d
D
∗
Alg
o
ri
th
m
fo
r
P
a
th
P
la
n
n
i
n
g
,
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I
FA
C
-
Pa
p
e
rs
On
L
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v
o
l
.
4
9
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n
o
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1
,
p
p
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o
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0
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1
0
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6
/
j.
ifac
o
l.
2
0
1
6
.
0
3
.
0
4
9
.
[2
7
]
M
.
El
b
a
n
h
a
wi
a
n
d
M
.
S
imic
,
"
S
a
m
p
li
n
g
-
Ba
se
d
R
o
b
o
t
M
o
ti
o
n
P
lan
n
i
n
g
:
A
Re
v
iew
,
"
in
I
EE
E
Acc
e
ss
,
v
o
l.
2
,
p
p
.
5
6
-
7
7
,
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0
1
4
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o
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1
0
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1
1
0
9
/AC
CES
S
.
2
0
1
4
.
2
3
0
2
4
4
2
.
[2
8
]
S
.
K
.
D
e
b
n
a
t
h
,
R
.
O
m
a
r
,
a
n
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N
.
B
.
A
.
L
a
t
i
p
,
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A
R
e
v
i
e
w
o
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E
n
e
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g
y
E
f
f
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c
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t
P
a
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l
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n
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A
l
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U
n
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a
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n
e
d
A
i
r
V
e
h
i
c
l
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s
,
”
C
o
m
p
u
t
a
t
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o
n
a
l
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n
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p
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5
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o
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:
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0
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7
/
9
7
8
-
981
-
13
-
2622
-
6
_
5
1
.
[2
9
]
J.
Ya
n
g
,
P
.
Dy
m
o
n
d
,
a
n
d
M
.
Je
n
k
in
,
“
P
ra
c
ti
c
a
li
ty
-
Ba
se
d
P
r
o
b
a
b
il
isti
c
Ro
a
d
m
a
p
s
M
e
th
o
d
,
”
2
0
1
1
C
a
n
a
d
i
a
n
Co
n
fer
e
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c
e
o
n
Co
m
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ter
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p
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0
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1
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d
o
i:
1
0
.
1
1
0
9
/CRV.2
0
1
1
.
2
1
.
[3
0
]
S
.
Ka
ra
m
a
n
a
n
d
E.
F
ra
z
z
o
li
,
“
In
c
re
m
e
n
tal
S
a
m
p
li
n
g
-
b
a
se
d
Al
g
o
r
it
h
m
s
fo
r
Op
t
ima
l
M
o
ti
o
n
P
lan
n
in
g
,
”
R
o
b
o
ti
c
s:
S
c
ien
c
e
a
n
d
S
y
ste
ms
,
v
o
l.
6
,
p
p
.
2
6
7
-
2
7
4
,
2
0
1
1
,
d
o
i:
1
0
.
7
5
5
1
/mit
p
r
e
ss
/9
1
2
3
.
0
0
3
.
0
0
3
8
.
[3
1
]
D.
De
v
a
u
rs,
T.
S
imé
o
n
,
a
n
d
J.
Co
rtés
,
“
Op
ti
m
a
l
P
a
th
P
lan
n
in
g
in
C
o
m
p
lex
Co
st
S
p
a
c
e
s
wit
h
S
a
m
p
li
n
g
-
Ba
se
d
Alg
o
rit
h
m
s,”
IEE
E
T
ra
n
sa
c
ti
o
n
s
o
n
A
u
to
m
a
ti
o
n
S
c
ien
c
e
a
n
d
E
n
g
i
n
e
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rin
g
,
v
o
l
.
1
3
,
n
o
.
2
,
p
p
.
4
1
5
-
4
2
4
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0
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6
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o
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0
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1
1
0
9
/T
ASE
.
2
0
1
5
.
2
4
8
7
8
8
1
.
[3
2
]
D.
De
v
a
u
rs,
T.
S
imé
o
n
a
n
d
J.
Co
rtés
,
"
Op
ti
m
a
l
P
a
th
P
lan
n
in
g
in
Co
m
p
lex
C
o
st
S
p
a
c
e
s
Wi
th
S
a
m
p
li
n
g
-
Ba
se
d
Alg
o
rit
h
m
s,"
i
n
IE
EE
T
r
a
n
sa
c
ti
o
n
s
o
n
Au
t
o
ma
ti
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n
S
c
ien
c
e
a
n
d
En
g
i
n
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rin
g
,
v
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l.
1
3
,
n
o
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2
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p
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4
1
5
-
4
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ri
l
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6
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o
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9
/
TAS
E.
2
0
1
5
.
2
4
8
7
8
8
1
.
[3
3
]
D.
Hs
u
,
J.
-
C.
Lat
o
m
b
e
,
a
n
d
H
.
Ku
rn
iaw
a
ti
,
“
On
t
h
e
P
ro
b
a
b
il
isti
c
F
o
u
n
d
a
ti
o
n
s
o
f
P
ro
b
a
b
il
ist
ic
Ro
a
d
m
a
p
P
lan
n
i
n
g
,
”
T
h
e
In
ter
n
a
ti
o
n
a
l
J
o
u
rn
a
l
o
f
R
o
b
o
ti
c
s
Res
e
a
rc
h
,
v
o
l
.
2
5
,
n
o
.
7
,
p
p
.
6
2
7
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6
4
3
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0
0
6
,
d
o
i:
1
0
.
1
1
7
7
/0
2
7
8
3
6
4
9
0
6
0
6
7
1
7
4
.
[3
4
]
J.
Ba
rra
q
u
a
n
d
,
L.
Ka
v
ra
k
i,
J.
C.
Lato
m
b
e
,
R.
M
o
twa
n
i,
T.
Y.
Li
,
a
n
d
P
.
Ra
g
h
a
v
a
n
,
“
A
Ra
n
d
o
m
S
a
m
p
li
n
g
S
c
h
e
m
e
fo
r
P
a
t
h
P
lan
n
i
n
g
,
”
I
n
ter
n
a
ti
o
n
a
l
J
o
u
rn
a
l
o
f
Ro
b
o
ti
c
s
Res
e
a
rc
h
,
v
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l.
1
6
,
n
o
.
6
,
p
p
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7
5
9
-
7
7
4
,
1
9
9
7
,
d
o
i:
1
0
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1
1
7
7
/0
2
7
8
3
6
4
9
9
7
0
1
6
0
0
6
0
4
.
[3
5
]
Z.
Ko
n
g
a
n
d
B.
M
e
tt
ler,
“
A
S
u
r
v
e
y
o
f
M
o
ti
o
n
P
lan
n
i
n
g
Al
g
o
r
it
h
m
s
fro
m
th
e
P
e
rsp
e
c
ti
v
e
o
f
Au
to
n
o
m
o
u
s
UA
V
G
u
id
a
n
c
e
,
”
J
o
u
rn
a
l
o
f
In
telli
g
e
n
t
&
Ro
b
o
ti
c
S
y
ste
ms
,
v
o
l.
5
7
,
p
p
.
6
5
-
1
0
0
,
2
0
1
0
,
d
o
i:
1
0
.
1
0
0
7
/s1
0
8
4
6
-
0
0
9
-
9
3
8
3
-
1.
[3
6
]
Zh
iy
e
Lee
a
n
d
Xio
n
g
C
h
e
n
,
"
P
a
th
p
lan
n
in
g
a
p
p
r
o
a
c
h
b
a
se
d
o
n
p
ro
b
a
b
il
isti
c
ro
a
d
m
a
p
f
o
r
se
n
so
r
b
a
se
d
c
a
r
-
li
k
e
ro
b
o
t
in
u
n
k
n
o
wn
e
n
v
iro
n
m
e
n
ts,"
2
0
0
4
IE
EE
I
n
ter
n
a
ti
o
n
a
l
C
o
n
fer
e
n
c
e
o
n
S
y
ste
ms
,
M
a
n
a
n
d
Cy
b
e
rn
e
ti
c
s
(IE
EE
Ca
t
.
No
.
0
4
CH3
7
5
8
3
)
,
v
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2
0
0
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p
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2
9
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-
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9
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o
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0
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1
1
0
9
/ICS
M
C.
2
0
0
4
.
1
4
0
0
7
7
4
.
[3
7
]
M
.
U.
F
a
ro
o
q
,
Z
.
Z
iy
a
n
g
a
n
d
M
.
E
jaz
,
"
Q
u
a
d
ro
t
o
r
UA
Vs
F
ly
in
g
F
o
rm
a
ti
o
n
Re
c
o
n
fi
g
u
ra
t
io
n
with
C
o
ll
isi
o
n
Av
o
id
a
n
c
e
Us
in
g
P
r
o
b
a
b
il
isti
c
Ro
a
d
m
a
p
Alg
o
rit
h
m
,
"
2
0
1
7
In
t
e
rn
a
ti
o
n
a
l
Co
n
fer
e
n
c
e
o
n
C
o
mp
u
ter
S
y
ste
ms
,
El
e
c
tro
n
ics
a
n
d
C
o
n
tro
l
(ICC
S
E
C)
,
2
0
1
7
,
p
p
.
8
6
6
-
8
7
0
,
d
o
i:
1
0
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1
1
0
9
/ICCS
EC.
2
0
1
7
.
8
4
4
6
7
8
1
.
[3
8
]
J.
Li
,
S
.
X.
Ya
n
g
a
n
d
Z.
X
u
,
"
A
S
u
rv
e
y
o
n
R
o
b
o
t
P
a
t
h
P
lan
n
i
n
g
u
sin
g
Bio
-
i
n
sp
ire
d
Alg
o
rit
h
m
s,"
2
0
1
9
IEE
E
In
ter
n
a
t
io
n
a
l
C
o
n
fer
e
n
c
e
o
n
R
o
b
o
ti
c
s a
n
d
Bi
o
mime
ti
c
s (R
OBIO)
,
2
0
1
9
,
p
p
.
2
1
1
1
-
2
1
1
6
.
[3
9
]
M
.
Na
d
h
ir,
A.
Wah
a
b
,
S
.
Ne
fti
-
m
e
z
ian
i,
a
n
d
A.
At
y
a
b
i,
“
A
C
o
m
p
re
h
e
n
si
v
e
Re
v
iew
o
f
S
wa
rm
Op
ti
m
iza
ti
o
n
Alg
o
rit
h
m
s,”
PL
o
S
ONE
,
p
p
.
1
-
3
6
,
2
0
1
5
,
d
o
i:
1
0
.
1
3
7
1
/
jo
u
rn
a
l.
p
o
n
e
.
0
1
2
2
8
2
7
.
[4
0
]
V.
S
e
lv
i
a
n
d
S
.
Tam
il
n
a
d
u
,
“
Co
m
p
a
ra
ti
v
e
An
a
ly
sis
o
f
An
t
Co
l
o
n
y
a
n
d
P
a
rti
c
le
S
wa
rm
Op
ti
m
iza
ti
o
n
Tec
h
n
iq
u
e
s,”
In
ter
n
a
t
io
n
a
l
J
o
u
rn
a
l
o
f
C
o
mp
u
ter
Ap
p
l
ica
ti
o
n
s
,
v
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l.
5
,
n
o
.
4
,
p
p
.
1
-
6
,
2
0
1
0
,
d
o
i:
1
0
.
5
1
2
0
/
9
0
8
-
1
2
8
6
.
[4
1
]
T.
T.
M
a
c
,
C
.
Co
p
o
t,
D.
T.
Tran
,
a
n
d
R.
De
Ke
y
se
r,
“
He
u
risti
c
Ap
p
ro
a
c
h
e
s
i
n
R
o
b
o
t
P
a
th
P
lan
n
in
g
:
A
su
r
v
e
y
,
”
Ro
b
o
ti
c
s a
n
d
Au
t
o
n
o
mo
u
s S
y
ste
ms
,
v
o
l
.
8
6
,
p
p
.
1
3
-
2
8
,
2
0
1
6
,
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9
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3
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.
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ra
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.
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Walter,
A.
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.
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[5
6
]
X.
Wu
,
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.
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i,
Y.
Xie
,
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S
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C.
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[5
7
]
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Ya
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Wan
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