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Vo
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1
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,
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alia.
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m
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s
3
5
7
8
3
5
7
@
s
tu
d
en
t.r
m
i
t.e
d
u
.
au
1.
I
NT
RO
D
UCT
I
O
N
R
ec
en
t
y
ea
r
s
h
av
e
w
it
n
es
s
ed
t
h
e
d
ev
e
lo
p
m
en
t
o
f
r
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b
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o
r
ca
r
r
y
i
n
g
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d
if
f
er
e
n
t
m
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s
s
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s
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th
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ar
e
h
az
ar
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m
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Sear
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s
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am
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ta
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m
is
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i
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w
h
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e
e
m
p
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m
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t
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at
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s
a
f
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f
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u
m
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p
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.
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h
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f
ir
s
t
is
s
u
e
to
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d
r
ess
w
h
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s
e
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ch
in
g
an
en
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o
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m
e
n
t
u
s
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co
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p
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r
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r
ev
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th
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m
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llid
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t
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o
b
s
tacle
s
in
th
e
e
n
v
ir
o
n
m
e
n
t
[
1
]
.
Seco
n
d
ly
,
it
i
s
i
m
p
o
r
tan
t
to
m
i
n
i
m
is
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th
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f
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t
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s
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e
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y
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d
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m
e.
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e
m
ain
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b
j
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o
f
p
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s
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t
i
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m
,
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d
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r
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a
n
d
en
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g
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co
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s
u
m
p
tio
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[
2
]
.
C
o
n
s
e
q
u
e
n
tl
y
,
t
h
e
c
h
o
s
en
s
ea
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c
h
tr
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to
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h
o
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ld
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p
ti
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ized
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ac
co
u
n
t
f
o
r
th
e
m
e
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tio
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f
a
cto
r
s
.
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o
th
is
ai
m
,
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u
s
t
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ate
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it
h
o
n
e
a
n
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th
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,
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ai
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l
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s
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ar
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n
g
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l sear
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d
ata
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ase,
to
g
u
ar
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tee
a
s
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a
n
d
ef
f
i
cien
t sear
c
h
an
d
co
v
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ag
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m
i
s
s
io
n
.
T
h
is
p
ap
er
ap
p
lies
g
r
id
-
b
ase
d
m
eth
o
d
f
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d
ef
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n
in
g
t
h
e
s
ea
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ch
en
v
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n
m
e
n
t.
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as
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eth
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s
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tili
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g
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g
r
ap
h
ica
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d
is
tr
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b
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ted
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ata
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ce
s
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to
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w
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th
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ac
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to
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s
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e
d
atab
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Su
ch
tech
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iq
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m
a
k
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o
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r
id
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ch
task
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d
also
to
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m
p
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v
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t
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ea
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ch
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.
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h
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w
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g
r
esear
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n
d
s
to
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o
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h
a
f
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r
e
m
en
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ed
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s
s
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es
in
th
e
f
ir
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t
p
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r
ap
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b
y
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p
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ased
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s
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ith
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.
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ith
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ter
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C
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v
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co
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ith
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m
s
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p
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.
T
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n
ex
t
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tio
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s
t
h
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elate
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p
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.
A
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liter
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
9
-
4856
IJ
RA
Vo
l.
6
,
No
.
1
,
Ma
r
ch
2
0
1
7
:
49
–
58
50
in
te
n
d
ed
co
n
tr
i
b
u
tio
n
o
f
t
h
e
p
r
esen
ted
r
esear
ch
to
t
h
e
f
ie
ld
o
f
k
n
o
w
led
g
e.
Fi
n
all
y
,
t
h
e
ef
f
icie
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o
f
t
h
e
p
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o
r
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s
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s
u
r
ed
an
d
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esu
lts
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.
2.
RE
L
AT
E
D
WO
RK
S
Ov
er
all,
t
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e
p
r
o
b
lem
o
f
co
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ativ
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p
ath
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n
n
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e
d
i
v
id
ed
in
to
f
o
u
r
s
u
b
-
p
r
o
b
le
m
s
;
ex
p
r
ess
io
n
o
f
t
h
e
s
ea
r
c
h
e
n
v
ir
o
n
m
e
n
t,
p
at
h
ca
lc
u
latio
n
,
p
ath
ex
ec
u
tio
n
an
d
also
co
m
m
u
n
icatio
n
b
et
wee
n
a
g
en
t
s
.
Mo
s
t
o
f
p
ath
p
lan
n
i
n
g
al
g
o
r
ith
m
s
a
v
ail
ab
le
in
th
e
liter
at
u
r
e
ar
e
b
ased
u
p
o
n
th
e
t
h
eo
r
y
o
f
v
is
ib
il
it
y
g
r
ap
h
s
[
3
]
,
Vo
r
o
n
o
i
d
iag
r
a
m
[
4
]
,
g
r
id
-
b
ased
[
5
]
an
d
ar
tif
icial
in
telli
g
en
ce
-
b
ased
[
6
]
m
et
h
o
d
s
ea
c
h
co
m
es
w
it
h
th
e
ad
v
a
n
ta
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d
s
h
o
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tco
m
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n
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s
.
Fo
r
i
n
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ta
n
ce
,
Vo
r
o
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o
i
d
iag
r
a
m
s
m
et
h
o
d
s
a
r
e
o
n
e
-
d
i
m
en
s
io
n
al
in
n
at
u
r
e
th
at
m
a
y
lead
to
an
in
ac
cu
r
ate
r
ep
r
esen
tatio
n
o
f
t
h
e
s
ea
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e
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w
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r
ad
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t
h
e
p
ath
p
lan
n
in
g
e
f
f
icie
n
c
y
[
7
]
.
On
t
h
e
o
t
h
er
h
an
d
,
ar
ti
f
ic
ial
i
n
telli
g
e
n
t
-
b
a
s
ed
m
eth
o
d
s
,
i.e
.
g
e
n
etic
al
g
o
r
ith
m
m
et
h
o
d
s
,
ar
e
p
r
o
v
en
to
b
e
s
u
itab
le
m
o
s
tl
y
f
o
r
h
an
d
li
n
g
s
m
all
-
s
ca
le
p
r
o
b
le
m
s
b
ec
a
u
s
e
o
f
th
e
ir
h
u
g
e
co
m
p
u
tatio
n
al
b
u
r
d
en
[
8
]
.
Am
o
n
g
t
h
e
i
n
tr
o
d
u
ce
d
m
et
h
o
d
s
p
r
ev
io
u
s
l
y
,
g
r
id
-
b
ased
m
et
h
o
d
s
ar
e
o
f
i
n
ter
es
t
o
f
th
i
s
r
es
ea
r
ch
,
s
i
n
ce
s
u
c
h
m
eth
o
d
s
ar
e
ea
s
y
to
s
et
-
u
p
,
f
as
t
an
d
r
eliab
le
i
n
co
m
p
ar
is
o
n
w
it
h
o
th
er
m
e
n
tio
n
ed
t
ec
h
n
iq
u
es.
Ko
ce
s
k
i
an
d
P
an
o
v
ap
p
lied
th
e
m
et
h
o
d
s
u
cc
ess
f
u
ll
y
to
a
g
r
id
d
ed
en
v
i
r
o
n
m
e
n
t
co
n
s
i
s
ted
o
f
o
b
s
tacle
o
cc
u
p
ied
an
d
e
m
p
t
y
ce
ll
s
[
9
]
.
T
o
f
in
d
th
e
o
p
ti
m
u
m
p
at
h
Dij
k
s
tr
a
alg
o
r
ith
m
h
as
b
ee
n
u
s
ed
;
h
o
w
e
v
er
,
th
e
m
et
h
o
d
h
as
it
s
o
w
n
d
is
ad
v
an
ta
g
es.
I
n
t
h
is
al
g
o
r
it
h
m
t
h
e
p
r
o
ce
s
s
in
g
o
f
t
h
e
d
ata
o
f
in
d
iv
id
u
al
ce
lls
o
f
t
h
e
en
v
ir
o
n
m
en
t
tak
es
to
o
lo
n
g
w
h
ic
h
i
n
t
u
r
n
d
e
g
r
ad
es
t
h
e
o
v
er
all
e
f
f
icie
n
c
y
o
f
t
h
e
a
lg
o
r
ith
m
.
T
o
o
v
er
co
m
e
th
i
s
p
r
o
b
lem
,
a
q
u
ad
tr
ee
m
et
h
o
d
h
as b
ee
n
d
ep
lo
y
ed
in
[
1
0
]
.
A
d
i
f
f
er
e
n
t
ap
p
r
o
ac
h
to
Dij
k
s
tr
a
alg
o
r
ith
m
,
k
n
o
w
n
a
s
A*
a
lg
o
r
ith
m
,
h
a
s
b
ee
n
u
s
ed
in
p
a
r
allel
w
i
th
th
e
Dij
k
s
tr
a
b
y
Z
h
a
n
g
a
n
d
Z
h
a
o
[
1
1
]
.
A
lth
o
u
g
h
,
t
h
e
p
r
o
ce
s
s
f
lo
w
o
f
t
h
e
al
g
o
r
ith
m
i
s
co
m
p
l
ex
,
s
o
t
h
at
e
f
f
icie
n
t
ap
p
licatio
n
o
f
A*
a
lg
o
r
it
h
m
r
eq
u
ir
es
d
ee
p
k
n
o
w
led
g
e
o
f
m
at
h
e
m
a
tics
.
T
h
e
co
m
b
i
n
atio
n
o
f
h
o
r
m
o
n
e
-
i
n
s
p
ir
ed
p
ath
p
lan
n
in
g
m
et
h
o
d
s
,
a
k
in
d
o
f
g
r
id
ce
ll
m
ar
k
in
g
i.e
.
b
y
n
u
m
er
ical
v
al
u
e
etc.
,
an
d
th
e
g
ir
d
b
ased
m
eth
o
d
s
ca
n
p
r
o
v
id
e
an
o
p
tim
u
m
s
ea
r
ch
alg
o
r
ith
m
[
1
2
]
.
T
h
e
m
ar
k
i
n
g
o
f
m
ap
ce
lls
en
ab
le
th
e
r
o
b
o
ts
f
o
r
u
p
d
atin
g
a
s
ec
tio
n
o
f
th
e
m
ap
t
h
at
m
a
y
c
o
n
tain
d
if
f
er
e
n
t
s
o
r
ts
o
f
i
n
f
o
r
m
atio
n
a
n
d
d
ata,
s
u
c
h
a
s
co
m
p
u
ls
o
r
y
o
p
er
atio
n
s
,
h
az
ar
d
w
ar
n
i
n
g
s
a
n
d
also
n
u
m
b
er
o
f
ti
m
e
s
ea
ch
r
o
b
o
t
s
ea
r
ch
es
t
h
at
p
ar
ticu
lar
ar
ea
.
T
h
e
o
v
er
all
ef
f
icien
c
y
o
f
th
is
m
et
h
o
d
b
ec
o
m
es
b
etter
as
th
e
n
u
m
b
er
o
f
s
ea
r
c
h
i
n
g
r
o
b
o
ts
in
cr
ea
s
es;
h
o
w
ev
er
,
t
h
e
i
n
cr
ea
s
e
in
n
u
m
b
er
o
f
s
ea
r
ch
i
n
g
a
g
en
ts
r
aise t
h
e
co
m
p
u
tatio
n
b
u
r
d
en
f
o
r
p
ath
p
lan
n
in
g
.
Gen
er
all
y
s
p
ea
k
in
g
,
t
h
e
c
u
r
r
en
t
li
ter
atu
r
e
s
till
d
e
m
an
d
s
co
m
p
r
e
h
e
n
s
i
v
e
r
esear
c
h
es
f
o
r
c
o
n
s
id
er
in
g
m
aj
o
r
ity
o
f
t
h
e
p
ar
a
m
eter
s
i
n
v
o
lv
ed
in
a
r
ea
l
s
ea
r
c
h
m
is
s
io
n
alto
g
eth
er
.
T
h
is
p
ap
er
p
r
esen
ts
a
n
o
v
el
r
esear
ch
,
s
in
ce
i
t
co
v
er
s
i
s
s
u
es
s
u
c
h
a
s
s
ea
r
ch
d
u
r
atio
n
m
i
n
i
m
izati
o
n
,
s
ea
r
ch
tr
aj
ec
to
r
y
o
v
er
lap
p
in
g
m
i
n
i
m
izatio
n
,
ef
f
icien
t
d
ata
tr
an
s
f
er
r
in
g
a
m
o
n
g
a
g
en
t
s
,
alo
n
g
s
id
e
i
m
p
le
m
en
tatio
n
o
f
a
r
ea
l
-
ti
m
e
s
ea
r
ch
m
is
s
io
n
s
i
m
u
latio
n
o
f
a
s
tatic
e
n
v
ir
o
n
m
e
n
t
i
n
C
+
+
en
v
ir
o
n
m
e
n
t.
C
o
n
s
eq
u
e
n
tl
y
,
th
e
p
r
o
p
o
s
ed
r
esear
ch
p
r
o
v
id
es
s
o
lu
tio
n
f
o
r
all
o
f
f
o
u
r
s
u
b
-
p
r
o
b
le
m
s
m
e
n
tio
n
ed
ea
r
lier
in
th
i
s
s
ec
tio
n
.
3.
P
RO
B
L
E
M
ST
AT
E
M
E
NT
B
ef
o
r
e
im
p
le
m
e
n
ti
n
g
a
r
o
b
o
tic
s
ea
r
ch
m
i
s
s
io
n
,
it
i
s
i
m
p
o
r
tan
t
to
s
i
m
u
late
t
h
e
w
h
o
le
m
is
s
io
n
u
s
i
n
g
co
m
p
u
ter
s
.
As
a
co
n
s
eq
u
e
n
ce
,
g
e
n
er
al
s
h
o
r
tco
m
i
n
g
s
o
f
a
s
ea
r
ch
s
c
h
e
m
e
ar
e
id
en
ti
f
ied
a
n
d
p
r
o
b
ab
le
f
ail
u
r
es
lik
e
d
a
m
a
g
es
to
r
o
b
o
ts
d
u
e
to
co
llis
io
n
ca
n
b
e
p
r
ev
en
ted
.
I
t
is
h
o
w
e
v
er
,
i
m
p
o
r
tan
t
to
n
o
t
e
th
e
li
m
itatio
n
s
o
f
a
r
o
b
o
tic
s
ea
r
ch
m
is
s
io
n
s
i
m
u
la
tio
n
,
w
h
ic
h
h
i
n
d
er
th
e
r
ea
lis
ti
c
ev
al
u
at
io
n
o
f
t
h
e
m
i
s
s
io
n
.
An
e
f
f
icie
n
t
r
o
b
o
tic
s
ea
r
ch
s
i
m
u
latio
n
m
u
s
t
co
n
s
id
er
th
e
r
ea
l
s
ea
r
ch
s
ce
n
a
r
io
s
s
u
ch
as
p
r
esen
ce
o
f
o
b
s
tacle
s
in
s
ea
r
c
h
en
v
ir
o
n
m
e
n
t,
li
m
ita
tio
n
s
in
c
o
m
m
u
n
icatio
n
r
a
n
g
e
o
f
s
ea
r
c
h
ag
e
n
t
s
in
co
o
p
er
ativ
e
s
ea
r
c
h
m
is
s
io
n
s
,
e
n
er
g
y
co
n
s
u
m
p
tio
n
m
a
n
a
g
e
m
e
n
t,
etc
.
T
h
e
f
o
llo
w
i
n
g
r
esear
ch
i
n
te
n
d
s
to
p
r
o
v
id
e
a
r
ea
lis
tic
co
o
p
e
r
ativ
e
s
ea
r
ch
s
i
m
u
lat
io
n
alg
o
r
it
h
m
ca
lled
“
d
ig
ital
en
v
ir
o
n
m
e
n
t
m
ar
k
i
n
g
”,
b
y
ad
d
r
ess
in
g
m
o
s
t
o
f
a
f
o
r
e
m
en
t
io
n
ed
li
m
itatio
n
s
.
I
n
t
h
is
r
esear
c
h
s
ea
r
ch
en
v
ir
o
n
m
e
n
t
i
s
r
ep
r
esen
ted
b
y
g
r
id
o
f
id
en
tical
d
i
g
itall
y
m
ar
k
ed
ce
lls
,
ea
ch
co
n
tain
s
n
u
m
b
er
o
f
ti
m
es
th
e
ce
ll
w
a
s
b
ein
g
s
ea
r
c
h
ed
.
R
o
b
o
ts
c
an
m
o
v
e
b
et
w
ee
n
ad
j
ac
en
t
ce
lls
i
n
cl
u
d
in
g
d
iag
o
n
all
y
p
lace
d
ce
lls
;
w
h
ile,
t
h
e
y
h
av
e
li
m
i
ted
in
f
o
r
m
atio
n
ab
o
u
t
th
e
o
t
h
er
ag
e
n
ts
s
u
r
v
e
y
i
n
g
th
e
ar
ea
.
Du
r
in
g
t
h
e
s
ea
r
ch
m
is
s
io
n
,
in
f
o
r
m
atio
n
ca
n
b
e
e
x
ch
a
n
g
ed
if
r
o
b
o
ts
ar
e
clo
s
e
e
n
o
u
g
h
to
ea
ch
o
th
er
.
Fo
u
r
s
ea
r
ch
al
g
o
r
ith
m
s
b
ased
o
n
d
i
g
ital
en
v
ir
o
n
m
e
n
t
m
ar
k
i
n
g
co
n
ce
p
t
ar
e
p
r
o
p
o
s
ed
.
Fin
all
y
,
t
h
e
g
o
al
is
th
at
ea
ch
ce
ll
i
s
v
i
s
ited
at
least
o
n
ce
b
y
an
y
o
f
th
e
r
o
b
o
ts
as so
o
n
as p
o
s
s
ib
le.
T
h
e
n
o
v
elt
y
o
f
t
h
e
p
r
esen
ted
r
esear
ch
ca
n
b
e
co
n
cl
u
d
ed
in
th
e
f
o
llo
w
in
g
li
n
es:
a.
I
n
th
e
p
r
o
p
o
s
ed
m
eth
o
d
,
th
e
o
b
s
tacle
s
ca
n
b
e
d
ef
in
ed
ac
cu
r
atel
y
,
w
h
er
ea
s
th
e
p
o
l
y
g
o
n
d
iv
is
io
n
o
f
t
h
e
en
v
ir
o
n
m
e
n
t
m
eth
o
d
[
1
3
]
lack
s
s
u
c
h
a
ch
ar
ac
ter
is
tic.
T
h
is
h
elp
s
in
p
r
o
d
u
cin
g
a
r
ea
lis
tic
s
i
m
u
latio
n
o
f
t
h
e
m
is
s
io
n
.
b.
Sin
ce
th
e
n
u
m
b
er
of
ti
m
es
a
ce
ll
in
t
h
e
en
v
ir
o
n
m
en
t
is
s
ea
r
c
h
ed
is
s
p
ec
i
f
ied
,
th
e
r
o
b
o
ts
ca
n
ea
s
il
y
d
e
t
e
c
t
th
e
ce
lls
th
o
s
e
h
a
v
e
b
ee
n
s
ea
r
ch
ed
less
.
T
h
is
ap
p
r
o
ac
h
ev
e
n
tu
all
y
r
ed
u
ce
s
t
h
e
o
v
er
lap
p
in
g
o
f
t
h
e
s
ea
r
ch
tr
aj
ec
to
r
ies an
d
also
m
i
s
s
io
n
d
u
r
atio
n
.
Evaluation Warning : The document was created with Spire.PDF for Python.
IJ
RA
I
SS
N:
2089
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Mis
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(
V
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)
51
c.
I
n
th
e
in
tr
o
d
u
ce
d
m
et
h
o
d
s
,
ea
ch
r
o
b
o
t
s
av
es
an
d
p
r
o
ce
s
s
es
th
e
r
ec
o
r
d
ed
s
ea
r
ch
d
ata
(
ce
ll
s
’
i
n
f
o
r
m
atio
n
)
in
d
ep
en
d
en
tl
y
a
n
d
u
p
d
ates
it
s
o
w
n
d
ata
-
b
ase
o
n
l
y
w
h
e
n
it
ap
p
r
o
ac
h
es
o
th
er
r
o
b
o
ts
;
th
er
ef
o
r
e,
in
ca
s
e
o
f
th
e
m
alf
u
n
ctio
n
in
g
o
f
o
n
e
o
r
m
o
r
e
r
o
b
o
ts
,
th
e
r
e
s
t
o
f
th
e
p
r
o
p
er
ly
f
u
n
ctio
n
i
n
g
r
o
b
o
ts
ca
n
ca
r
r
y
o
u
t
t
h
e
s
ea
r
ch
m
is
s
io
n
to
t
h
e
en
d
w
it
h
o
u
t
a
n
y
l
i
m
itatio
n
.
T
h
i
s
k
i
n
d
o
f
en
co
u
n
ter
w
it
h
p
o
s
s
ib
le
ac
cid
en
ts
h
as
n
o
t
b
ee
n
d
ea
lt
w
it
h
in
t
h
e
o
th
er
s
i
m
ilar
m
eth
o
d
s
s
u
c
h
as
th
e
m
e
th
o
d
s
b
ased
o
n
th
e
ce
n
tr
alize
d
lear
n
in
g
m
et
h
o
d
s
[
1
4
]
.
d.
T
h
e
alg
o
r
ith
m
s
d
o
n
o
t
r
eq
u
ir
e
th
e
r
o
b
o
ts
to
b
e
in
to
u
ch
with
ea
ch
o
th
er
all
t
h
e
ti
m
e,
o
r
ev
en
w
it
h
th
e
ce
n
tr
al
o
p
er
ato
r
.
T
h
er
ef
o
r
e,
th
e
o
p
ti
m
u
m
u
s
e
o
f
t
h
e
co
m
m
u
n
icat
io
n
al
r
ec
ei
v
er
s
an
d
tr
an
s
m
itter
s
ca
n
g
r
ea
tl
y
i
m
p
r
o
v
e
t
h
e
en
er
g
y
co
n
s
u
m
p
t
i
o
n
.
L
ac
k
o
f
a
ce
n
tr
al
o
p
er
ato
r
,
o
r
in
o
th
er
w
o
r
d
,
th
i
s
au
to
n
o
m
o
u
s
l
y
f
u
n
ctio
n
i
n
g
m
et
h
o
d
m
i
n
i
m
izes
th
e
h
u
m
an
i
n
ter
v
e
n
tio
n
.
e.
Usi
n
g
a
n
in
n
o
v
at
iv
e
d
ata
ex
ch
an
g
e
tec
h
n
iq
u
e,
ca
lled
“n
e
ar
est
-
ze
r
o
”
alg
o
r
ith
m
,
g
r
ea
tl
y
i
m
p
r
o
v
es
t
h
e
co
o
p
er
atio
n
ef
f
icien
c
y
b
et
w
e
en
r
o
b
o
t
s
w
h
ic
h
r
esu
lt
s
in
an
o
p
ti
m
ized
co
m
p
u
tatio
n
b
u
r
d
en
o
f
th
e
alg
o
r
ith
m
a
n
d
also
a
s
h
o
r
t sear
ch
m
is
s
io
n
.
T
h
e
f
o
llo
w
i
n
g
s
ec
tio
n
e
x
p
l
ain
s
th
e
g
e
n
er
al
m
eth
o
d
o
lo
g
y
o
f
t
h
e
r
esear
c
h
.
Fo
u
r
g
r
id
-
b
ased
co
o
p
er
ativ
e
s
ea
r
ch
al
g
o
r
ith
m
s
ar
e
th
e
n
i
n
tr
o
d
u
ce
d
a
n
d
co
m
p
ar
is
o
n
is
m
ad
e
b
et
w
ee
n
th
e
m
i
n
ter
m
s
o
f
s
ea
r
c
h
tr
aj
ec
to
r
y
o
v
er
lap
p
in
g
a
n
d
als
o
m
i
s
s
io
n
d
u
r
atio
n
t
i
m
e
-
s
tep
.
4.
RE
S
E
ARCH
M
E
T
H
O
D
T
h
is
s
ec
tio
n
co
n
tain
s
ex
p
la
n
at
io
n
s
o
n
r
ep
r
esen
t
in
g
t
h
e
s
ea
r
c
h
en
v
ir
o
n
m
e
n
t,
in
tr
o
d
u
cin
g
th
e
s
tr
u
ct
u
r
e
o
f
t
h
e
al
g
o
r
ith
m
s
,
p
ath
p
lan
n
i
n
g
o
f
s
ea
r
c
h
a
g
en
ts
,
a
n
d
t
h
e
w
a
y
t
h
e
y
i
n
ter
ac
t
an
d
co
o
p
er
ate
w
it
h
ea
c
h
o
t
h
er
.
T
h
e
w
h
o
le
s
ea
r
ch
m
is
s
io
n
h
as
b
ee
n
s
i
m
u
lated
u
s
i
n
g
C
++
p
r
o
g
r
a
m
m
i
n
g
e
n
v
ir
o
n
m
e
n
t
an
d
ar
tif
icial
i
n
telli
g
e
n
ce
p
r
o
g
r
am
m
i
n
g
tech
n
iq
u
e
s
,
w
h
ich
in
c
lu
d
es
s
i
m
u
la
tio
n
o
f
t
h
e
r
o
b
o
ts
’
p
ath
p
lan
n
i
n
g
,
t
h
e
en
v
ir
o
n
m
e
n
t,
t
h
e
s
en
s
o
r
s
’
r
an
g
e,
an
d
t
h
e
w
a
y
r
o
b
o
ts
co
o
p
er
ate.
I
n
th
is
r
esear
c
h
,
it h
a
s
b
ee
n
ass
u
m
ed
th
at:
a.
T
h
e
o
b
s
tacle
s
in
th
e
e
n
v
ir
o
n
m
en
t a
r
e
all
s
tatic,
b.
Ob
s
tacle
s
h
av
e
b
ee
n
co
n
s
id
er
ed
as a
co
llectio
n
o
f
s
q
u
ar
e
ce
ll
s
.
I
f
th
e
o
b
s
tacle
s
ize
is
s
m
alle
r
th
e
ce
ll si
ze
,
th
en
t
h
e
w
h
o
le
ce
ll is
co
n
s
id
er
ed
as a
n
o
b
s
tacle
.
c.
T
h
e
s
ea
r
ch
tr
aj
ec
to
r
y
o
f
ea
ch
r
o
b
o
t c
o
n
s
is
ts
o
f
s
e
g
m
en
t
s
.
d.
T
h
e
co
m
m
u
n
icatio
n
r
an
g
e
o
f
t
h
e
r
o
b
o
ts
is
li
m
i
ted
.
E
ac
h
r
o
b
o
t in
f
o
r
m
s
t
h
e
o
th
er
r
o
b
o
ts
o
f
it
s
d
ec
is
io
n
s
o
n
l
y
w
h
e
n
it is
i
n
th
e
co
m
m
u
n
i
ca
tio
n
al
r
an
g
e
o
f
t
h
e
m
.
T
h
e
d
ec
is
io
n
m
a
k
i
n
g
a
n
d
o
b
s
tacle
s
en
s
i
n
g
d
ela
y
ti
m
e
h
a
s
b
ee
n
ig
n
o
r
ed
;
th
er
e
f
o
r
e
it
d
o
es
n
o
t
af
f
ec
t
th
e
ca
lcu
latio
n
ti
m
e
o
f
a
p
at
h
.
5.
AL
G
O
RI
T
H
M
DE
SI
G
N
W
h
en
ad
d
r
ess
i
n
g
f
o
r
m
at
h
e
m
atica
l
m
o
d
elin
g
o
f
t
h
e
o
b
s
tacle
s
,
b
ef
o
r
e
s
tar
tin
g
t
h
e
s
i
m
u
latio
n
,
it
s
u
f
f
ices
to
as
s
i
g
n
a
h
i
g
h
v
alu
e
to
t
h
e
ce
ll
s
w
i
th
o
b
s
tacle
s
(
Na
m
el
y
9
9
9
o
r
9
9
9
9
,
etc.
)
an
d
“
0
”
to
t
h
e
ce
lls
w
it
h
o
u
t
o
b
s
tacle
s
in
th
e
n
u
m
e
r
ical
f
ield
m
ap
o
f
th
e
en
v
ir
o
n
m
en
t.
Af
ter
th
e
s
i
m
u
latio
n
s
ta
r
ts
,
ea
ch
ti
m
e
th
at
a
ce
ll
is
b
ei
n
g
s
ea
r
ch
ed
b
y
r
o
b
o
ts
,
its
v
al
u
e
i
n
cr
ea
s
es
b
y
o
n
e
u
n
i
t.
T
h
er
ef
o
r
e,
th
e
n
u
m
er
ical
v
al
u
e
o
f
a
ce
ll
at
a
g
iv
e
n
iter
atio
n
in
d
icate
s
th
e
n
u
m
b
er
o
f
ti
m
e
s
th
at
t
h
e
ce
ll
h
a
s
b
ee
n
s
ea
r
ch
ed
u
n
ti
l th
at
iter
a
tio
n
.
I
n
g
en
er
al,
d
iv
id
in
g
th
e
e
n
v
ir
o
n
m
e
n
t
i
n
to
ce
lls
an
d
th
e
n
ec
ess
it
y
o
f
co
o
p
er
atio
n
b
et
w
ee
n
th
e
ag
e
n
ts
r
eq
u
ir
es
th
at
ea
ch
a
g
en
t
to
d
ea
l
w
it
h
9
ce
lls
s
i
m
u
ltan
eo
u
s
l
y
.
T
h
e
alg
o
r
ith
m
s
tar
ts
w
it
h
t
h
e
r
o
b
o
ts
s
itu
ated
at
th
eir
i
n
itial
p
o
s
itio
n
s
.
As
t
h
e
s
ea
r
ch
m
i
s
s
io
n
s
tar
t
s
,
ea
ch
r
o
b
o
t
s
ea
r
ch
es
it
s
8
n
ei
g
h
b
o
r
in
g
c
ells
an
d
t
h
e
n
m
o
v
e
s
to
ce
ll
w
it
h
m
in
i
m
u
m
v
a
lu
e
as
s
h
o
w
n
i
n
Fi
g
u
r
e
1
.
*
*
*
*
A
*
*
*
*
Fig
u
r
e
1
.
E
ig
h
t d
i
f
f
er
e
n
t c
h
o
ices (
C
ells
i
n
d
icate
d
b
y
“*
”)
f
o
r
th
e
m
o
v
e
m
en
t o
f
th
e
r
o
b
o
t A
I
f
t
h
e
m
i
n
i
m
u
m
v
al
u
es
o
f
s
ev
er
al
n
ei
g
h
b
o
r
in
g
ce
ll
s
ar
e
t
h
e
s
a
m
e,
al
g
o
r
it
h
m
ch
o
o
s
e
s
o
n
e
o
f
t
h
e
m
at
r
an
d
o
m
.
As
t
h
e
r
o
b
o
ts
co
m
e
ap
p
r
o
ac
h
ea
ch
o
th
er
s
o
th
at
t
h
eir
p
o
s
itio
n
b
ec
o
m
es
w
i
th
in
th
e
s
e
n
s
i
n
g
r
an
g
e
o
f
th
e
o
th
er
r
o
b
o
ts
,
th
e
y
ex
c
h
a
n
g
e
th
eir
r
ec
o
r
d
ed
s
ea
r
ch
d
ata
an
d
u
p
d
ate
th
eir
o
w
n
m
ap
s
o
f
th
e
en
v
ir
o
n
m
en
t
s
o
th
at
t
h
e
y
all
b
ec
o
m
e
id
e
n
tical.
As it
w
i
ll b
e
s
ee
n
later
,
th
e
wa
y
a
r
o
b
o
t c
o
m
b
i
n
es it
s
o
w
n
d
ata
w
ith
th
o
s
e
th
at
it
r
ec
eiv
es
f
r
o
m
th
e
o
t
h
er
s
s
i
g
n
i
f
ican
tl
y
af
f
ec
t
s
th
e
p
er
f
o
r
m
an
c
e
o
f
th
e
al
g
o
r
ith
m
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
9
-
4856
IJ
RA
Vo
l.
6
,
No
.
1
,
Ma
r
ch
2
0
1
7
:
49
–
58
52
T
h
e
s
ea
r
ch
alg
o
r
ith
m
co
n
ti
n
u
es
u
n
til
all
t
h
e
ce
lls
h
a
v
e
b
ee
n
s
ea
r
ch
ed
an
d
f
in
al
l
y
w
h
e
n
ea
ch
r
o
b
o
t
s
ea
r
ch
es
t
h
e
las
t
“
0
”
v
al
u
e
ce
l
l,
th
e
m
is
s
io
n
ter
m
in
a
tes
a
n
d
th
e
n
u
m
b
er
o
f
s
ea
r
ch
i
ter
atio
n
s
(
s
ea
r
ch
d
u
r
atio
n
)
an
d
th
e
f
i
n
al
m
ap
i
n
cl
u
d
in
g
f
in
al
t
h
e
n
u
m
er
ical
v
a
lu
e
s
o
f
t
h
e
ce
lls
ar
e
s
e
n
t
to
t
h
e
p
r
in
te
r
as
th
e
o
u
tp
u
t.
T
h
e
p
s
eu
d
o
-
co
d
e
o
f
th
e
d
ig
ital
m
ar
k
in
g
m
et
h
o
d
is
as f
o
llo
w
s
:
1:
v
o
ids
ea
rc
hM
a
p
{
2:
L
et
ti
m
e
to
0
3:
f
o
r
all
r
o
b
o
ts
do
{
4:
if
all
th
e
ce
lls
h
av
e
b
ee
n
s
ea
r
c
h
ed
5:
re
t
urn
n
u
m
b
er
o
f
iter
atio
n
s
6:
L
et
A
r
o
u
n
d
[
8
]
to
th
e
v
al
u
e
o
f
th
e
eig
h
t
n
ei
g
h
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et=
ch
o
o
s
i
n
g
a
r
an
d
o
m
b
lo
ck
in
t
h
e
Mi
n
i
m
u
m
s
ar
r
a
y
11:
m
o
v
e
t
h
e
r
o
b
o
t p
o
s
itio
n
to
tar
g
et
12:
v
alu
e(
r
o
b
o
t.p
o
s
itio
n
)
=v
al
u
e(
r
o
b
o
t.p
o
s
itio
n
)
+
1
13:
f
o
r
i f
r
o
m
0
to
n
u
m
b
er
o
f
r
o
b
o
t
14:
if
th
is
R
o
b
o
t a
n
d
r
o
b
o
t[
i
]
ar
e
clo
s
e
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
9
-
4856
IJ
RA
Vo
l.
6
,
No
.
1
,
Ma
r
ch
2
0
1
7
:
49
–
58
54
15:
ch
an
g
eDa
ta(
t
h
is
R
o
b
o
t,
r
o
b
o
t[
i
]
)
16:
ti
m
e=
ti
m
e
+
1
17:
}
18:
}
Fig
u
r
e
4
.
Do
u
b
le
-
la
y
er
s
ea
r
ch
u
s
i
n
g
r
o
b
o
t A
(
E
ac
h
n
u
m
b
er
w
it
h
“*
”
id
en
ti
f
ie
s
a
s
p
ec
if
ic
c
ell
an
d
s
h
o
u
ld
n
o
t
co
n
f
u
s
ed
w
it
h
t
h
e
n
u
m
er
ica
l v
alu
e
o
f
t
h
e
ce
ll)
5
.
4
.
Nea
re
s
t
Z
er
o
-
P
o
int
Sea
rc
h
Alg
o
rit
h
m
A
n
o
v
el
p
at
h
p
lan
n
i
n
g
m
et
h
o
d
,
ca
lled
th
e
“n
ea
r
est
ze
r
o
-
p
o
in
t”
s
ea
r
ch
al
g
o
r
it
h
m
is
p
r
esen
ted
h
er
ein
.
I
n
t
h
is
a
lg
o
r
it
h
m
,
ea
ch
r
o
b
o
t,
n
o
t
o
n
l
y
s
ea
r
ch
e
s
t
h
e
n
u
m
er
i
ca
l
v
al
u
es
o
f
its
n
ei
g
h
b
o
r
in
g
ce
lls
,
b
u
t
also
,
i
f
i
t
n
ec
es
s
itates,
it
s
ea
r
c
h
es
e
v
er
y
ce
lls
in
t
h
e
en
v
ir
o
n
m
en
t
a
n
d
m
o
v
es
t
h
r
o
u
g
h
t
h
e
d
ir
ec
tio
n
to
th
e
n
ea
r
est
ce
l
l
t
h
at
h
a
s
n
o
t
y
et
b
ee
n
s
ea
r
c
h
e
d
.
I
n
f
ac
t,
ea
ch
ag
en
t
m
ea
s
u
r
es
th
e
d
is
ta
n
ce
o
f
its
8
n
eig
h
b
o
r
in
g
ce
lls
f
r
o
m
th
e
n
ea
r
est
ce
ll
y
et
to
b
e
s
ea
r
ch
ed
an
d
ch
o
o
s
e
s
a
n
eig
h
b
o
r
in
g
ce
ll
as
t
h
e
o
r
ig
in
o
f
it
s
n
ex
t
m
o
v
e
th
at
h
as
th
e
least
d
is
tan
ce
f
r
o
m
th
e
n
ea
r
est
ce
ll
w
it
h
ze
r
o
v
alu
e.
I
f
o
n
it
s
w
a
y
to
th
is
ce
ll,
t
h
e
ag
e
n
t
en
co
u
n
ter
s
o
th
er
ag
e
n
ts
,
th
e
y
s
tar
t
ex
c
h
an
g
i
n
g
th
eir
s
t
o
r
ed
d
ata
an
d
if
it
f
i
n
d
s
th
at
o
n
e
o
f
th
e
s
e
r
o
b
o
ts
is
g
o
in
g
to
s
ea
r
ch
a
ce
ll
th
at
it
h
ad
alr
ea
d
y
in
ten
d
ed
to
s
ea
r
c
h
,
ch
a
n
g
es
it
s
p
ath
a
n
d
m
o
v
e
s
to
w
ar
d
s
th
e
n
ea
r
est
ce
l
l
th
a
t
h
as
n
o
t
y
et
b
ee
n
s
ea
r
ch
ed
.
I
n
ca
s
e
t
h
e
n
ex
t
d
esti
n
a
tio
n
o
f
th
e
r
o
b
o
t
is
o
cc
u
p
ied
b
y
o
b
s
tacle
s
,
t
h
e
n
ei
g
h
b
o
r
in
g
ce
ll
w
i
t
h
m
i
n
i
m
u
m
v
al
u
e
th
at
is
s
till
i
n
n
ea
r
es
t
d
is
ta
n
ce
to
a
ze
r
o
ce
ll
w
ill
b
e
s
elec
ted
.
T
h
e
p
s
eu
d
o
-
co
d
e
f
o
r
th
e
“
Nea
r
est
-
Z
er
o
P
o
in
t”
Alg
o
r
it
h
m
r
ea
d
s
as f
o
llo
w
s
:
1:
f
lo
a
t
dis
t
T
o
Nea
re
s
t
Z
er
o
P
la
c
e(
p
o
s
itio
n
a
)
{
2:
L
et
Min
e
to
10000
3:
f
o
r
i f
r
o
m
0
to
r
o
w
4:
f
o
r
j
f
r
o
m
0
to
co
lu
m
n
5:
if
m
in
<
(
i
-
a.
x
)
^
2
+
(j
-
a.
y
)
^
2
6:
m
i
n
=(
i
-
a.
x
)
^
2
+
(
j
-
a.
y
)
^
2
7
re
t
urn
m
in
e
8:
}
9:
v
o
id
s
ea
rc
hM
a
p
{
10:
L
et
ti
m
e
to
0
11:
L
et
m
i
n
es[]
12:
f
o
r
all
R
o
b
o
t
do
13:
if
all
th
e
ce
lls
i
n
t
h
e
m
ap
ar
e
s
ea
r
ch
ed
14:
re
t
urn
n
u
m
b
er
o
f
iter
atio
n
s
;
15:
L
et
A
r
o
u
n
d
[
8
]
to
th
e
d
is
tT
o
N
ea
r
estZ
er
o
P
lace
o
f
th
e
ei
g
h
t c
ells
ar
o
u
n
d
th
i
s
R
o
b
o
t
16:
L
et
Min
i
m
u
m
s
[
]
to
th
e
m
in
i
m
u
m
s
o
f
t
h
e
A
r
o
u
n
d
ar
r
ay
17:
tar
g
et=
ch
o
o
s
i
n
g
a
r
an
d
o
m
b
lo
ck
in
t
h
e
Mi
n
i
m
u
m
s
ar
r
a
y
18:
m
o
v
e
t
h
e
r
o
b
o
t p
o
s
itio
n
to
tar
g
et
19:
v
alu
e(
r
o
b
o
t.p
o
s
itio
n
)
=v
al
u
e(
r
o
b
o
t.p
o
s
itio
n
)
+
1
20:
f
o
r
i f
r
o
m
to
n
u
m
b
er
o
f
R
o
b
o
t
21:
if
th
is
R
o
b
o
t
an
d
R
o
b
o
t[
i]
ar
e
c
lo
s
e
22:
ch
an
g
eDa
ta(
t
h
is
R
o
b
o
t,
r
o
b
o
t[
i
]
)
23:
ti
m
e=
ti
m
e
+
1
24:
}
6.
M
I
SS
I
O
N
SI
M
UL
AT
I
O
N
I
n
th
is
s
ec
tio
n
th
e
f
u
n
ctio
n
ali
t
y
an
d
ef
f
icie
n
c
y
o
f
a
f
o
r
e
m
e
n
tio
n
ed
alg
o
r
it
h
m
s
is
ev
a
lu
at
ed
.
T
o
th
is
en
d
,
f
o
u
r
s
i
m
u
latio
n
e
n
v
ir
o
n
m
en
ts
h
a
v
e
b
ee
n
d
ef
in
ed
.
T
h
e
d
i
m
e
n
s
io
n
s
o
f
th
e
en
v
ir
o
n
m
en
ts
ar
e
all
eq
u
al
to
Evaluation Warning : The document was created with Spire.PDF for Python.
IJ
RA
I
SS
N:
2089
-
4856
“
N
ea
r
es
t Zero
-
p
o
in
t” A
lg
o
r
ith
m
fo
r
C
o
o
p
era
tive
R
o
b
o
tic
S
ea
r
ch
Mis
s
io
n
s
(
V
a
h
id
A
r
ya
i
)
55
1
0
×1
5
an
d
th
e
y
d
if
f
er
o
n
l
y
i
n
th
e
n
u
m
b
er
an
d
d
is
tr
i
b
u
tio
n
o
f
th
e
o
b
s
tacle
s
.
I
n
t
h
e
f
ir
s
t
s
i
m
u
latio
n
ex
p
er
i
m
en
t,
th
e
e
n
v
ir
o
n
m
en
t
is
co
n
s
id
er
ed
w
i
th
n
o
o
b
s
tacle
s
a
s
s
h
o
w
n
in
Fi
g
u
r
e
5
(
a)
.
Su
c
h
a
n
e
n
v
i
r
o
n
m
e
n
t
p
r
o
v
id
es
a
p
o
s
s
ib
ilit
y
to
ex
a
m
i
n
e
t
h
e
e
f
f
icie
n
c
y
o
f
co
o
p
er
atio
n
a
m
o
n
g
ag
e
n
ts
u
s
i
n
g
ea
ch
a
lg
o
r
it
h
m
.
I
n
t
h
e
s
ec
o
n
d
s
i
m
u
lat
io
n
ex
p
er
i
m
e
n
t,
a
s
lab
co
v
er
in
g
t
h
r
ee
ce
lls
,
a
lo
ca
l
m
ax
i
m
u
m
o
r
,
in
o
th
er
w
o
r
d
s
,
a
p
o
ten
tial
b
ar
r
ier
[
1
4
]
,
h
as
b
ee
n
cr
ea
ted
as
s
h
o
w
n
i
n
Fi
g
u
r
e
5
(
b
)
.
I
n
th
e
th
ir
d
s
i
m
u
latio
n
ex
p
er
i
m
en
t,
a
s
e
co
n
d
s
lab
h
as
al
s
o
b
ee
n
ad
d
ed
,
s
o
th
e
e
f
f
icie
n
ci
e
s
o
f
th
e
alg
o
r
it
h
m
s
co
u
ld
b
e
s
t
u
d
ied
in
th
e
p
r
esen
ce
o
f
o
b
s
tacle
s
h
av
in
g
n
o
co
r
n
er
as
s
h
o
w
n
in
Fi
g
u
r
e
5
(
c
)
.
A
d
d
in
g
t
h
e
s
ec
o
n
d
o
b
s
tacle
h
as
d
ec
r
ea
s
ed
th
e
s
ea
r
ch
ar
ea
an
d
,
o
n
th
e
o
th
er
h
an
d
,
h
a
s
i
n
cr
ea
s
ed
th
e
n
u
m
b
er
o
f
th
e
lo
ca
l
m
ax
i
m
a.
T
h
er
ef
o
r
e,
th
e
en
co
u
n
ter
o
f
t
h
e
alg
o
r
ith
m
s
w
it
h
t
h
es
e
t
w
o
f
ac
to
r
s
co
u
ld
b
e
ch
ec
k
ed
.
I
n
th
e
f
o
u
r
th
s
i
m
u
latio
n
ex
p
er
i
m
e
n
t,
m
o
r
e
co
m
p
li
ca
ted
o
b
s
tacle
s
ar
e
in
tr
o
d
u
ce
d
a
n
d
th
er
e
f
o
r
e
t
h
e
s
tu
d
y
o
f
t
h
e
e
f
f
icie
n
cies
o
f
t
h
e
al
g
o
r
ith
m
s
i
n
d
ea
li
n
g
w
i
th
th
e
ce
lls
b
o
u
n
d
ed
f
r
o
m
t
h
r
ee
s
id
es b
ec
o
m
e
s
p
o
s
s
ib
le
as s
h
o
w
n
i
n
Fi
g
u
r
e
5
(
d
)
.
(
a)
(
b
)
(
c)
(
d
)
Fig
u
r
e
5
.
T
h
e
s
i
m
u
la
tio
n
e
n
v
ir
o
n
m
e
n
ts
a
n
d
th
e
i
n
itial p
o
s
it
io
n
s
o
f
t
h
e
r
o
b
o
ts
A
,
B
,
an
d
C
,
a
t th
e
o
n
s
e
t o
f
th
e
s
ea
r
ch
m
is
s
io
n
7.
RE
SU
L
T
S AN
D
D
I
SC
USS
I
O
N
T
h
e
r
esu
lt
s
o
f
t
h
e
s
i
m
u
lat
io
n
s
in
d
i
f
f
er
en
t
en
v
ir
o
n
m
e
n
ts
ar
e
p
r
esen
ted
i
n
t
h
is
s
ec
tio
n
.
T
ab
le
1
s
h
o
w
s
th
e
n
u
m
b
er
o
f
i
ter
atio
n
s
ta
k
es
f
o
r
ea
ch
o
n
e
o
f
m
e
n
tio
n
ed
alg
o
r
ith
m
s
to
co
m
p
letel
y
s
ea
r
ch
t
h
e
e
n
v
ir
o
n
m
en
t
s
A
to
D
as
s
h
o
w
n
i
n
Fi
g
u
r
e
5
u
s
i
n
g
t
h
r
ee
r
o
b
o
ts
.
T
h
e
r
esu
lts
r
e
v
ea
l
t
h
at
w
h
e
n
t
h
e
n
u
m
b
er
o
f
o
b
s
tacle
s
in
cr
ea
s
e
s
,
th
e
T
w
o
-
la
y
er
d
ata
e
x
ch
a
n
g
e
alg
o
r
ith
m
i
s
m
o
r
e
e
f
f
icie
n
t
t
h
a
n
t
h
e
Z
d
ata
ex
c
h
a
n
g
e
a
lg
o
r
it
h
m
w
h
ic
h
is
d
u
e
t
h
e
s
tr
u
ct
u
r
al
d
if
f
er
en
ce
b
et
w
ee
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CO
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Sev
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e
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[
1
5
,
1
6
]
.
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n
.
RE
F
E
R
E
NC
E
S
[
1
]
Zh
e
n
g
-
Hu
a
,
Y.,
e
t
a
l.
,
“
P
a
th
P
lan
n
i
n
g
f
o
r
Co
a
lm
in
e
Re
sc
u
e
Ro
b
o
t
Ba
se
d
o
n
Hy
b
rid
A
d
a
p
ti
v
e
Artif
icia
l
F
ish
S
w
a
r
m
A
l
g
o
rit
h
m
”
,
In
d
o
n
e
sia
n
J
o
u
rn
a
l
o
f
El
e
c
trica
l
En
g
in
e
e
rin
g
a
n
d
Co
m
p
u
ter
S
c
ien
c
e
,
1
2
(
1
0
)
:
7
2
2
3
-
7
2
3
2
,
2
0
1
4
.
[
2
]
Be
n
a
o
u
m
e
u
r,
I.
,
e
t
a
l.
,
“
B
a
c
k
st
e
p
p
in
g
A
p
p
ro
a
c
h
f
o
r
A
u
to
n
o
m
o
u
s M
o
b
i
le Ro
b
o
t
T
ra
jec
to
r
y
T
r
a
c
k
in
g
”
,
In
d
o
n
e
sia
n
J
o
u
rn
a
l
o
f
El
e
c
trica
l
En
g
in
e
e
rin
g
a
n
d
Co
m
p
u
ter
S
c
ien
c
e
,
2
(
3
),
2
0
1
6
.
[
3
]
Hu
a
n
g
,
H.
P
.
a
n
d
C
h
u
n
g
,
S
.
Y
.
,
“
D
y
n
a
m
ic
v
isib
il
it
y
g
ra
p
h
f
o
r
p
a
th
p
lan
n
in
g
.
I
n
In
telli
g
e
n
t
Ro
b
o
ts
a
n
d
S
y
ste
m
s”
,
2
0
0
4
.
(IROS
2
0
0
4
)
.
Pro
c
e
e
d
in
g
s.
2
0
0
4
IEE
E/
RS
J
I
n
ter
n
a
ti
o
n
a
l
C
o
n
fer
e
n
c
e
o
n
(
Vo
l.
3
,
p
p
.
2
8
1
3
-
2
8
1
8
).
IE
EE
.
2
0
0
4
.
[
4
]
Bh
a
tt
a
c
h
a
ry
a
,
P
.
a
n
d
G
a
v
ril
o
v
a
,
M
.
L
.
,
“
Ro
a
d
m
a
p
-
b
a
se
d
p
a
th
p
lan
n
in
g
-
Us
in
g
th
e
Vo
ro
n
o
i
d
iag
ra
m
f
o
r
a
c
lea
ra
n
c
e
-
b
a
se
d
sh
o
rtes
t
p
a
th
.
”
,
I
EE
E
Ro
b
o
ti
c
s
&
Au
to
ma
ti
o
n
M
a
g
a
zin
e
,
1
5
(
2
),
p
p
.
5
8
-
6
6
,
2
0
0
8
.
[
5
]
Zh
o
u
S
h
a
o
,
Da
v
id
T
a
n
iar,
Kik
i
M
a
u
lan
a
A
d
h
in
u
g
ra
h
a
,
“
V
o
r
o
n
o
i
-
b
a
se
d
Ra
n
g
e
-
NN
se
a
r
c
h
w
it
h
M
a
p
G
rid
in
a
m
o
b
il
e
e
n
v
iro
n
m
e
n
t”,
Fu
tu
re
G
e
n
e
ra
ti
o
n
Co
mp
u
ter
S
y
ste
ms
,
Vo
lu
m
e
6
7
,
P
a
g
e
s
3
0
5
-
3
1
4
,
2
0
1
6
,
IS
S
N
0
1
6
7
-
7
3
9
X.
[
6
]
Ka
p
a
n
o
g
lu
,
M
.
,
A
li
k
a
l
f
a
,
M
.
,
Oz
k
a
n
,
M
.
,
Ya
z
ıcı,
A
.
&
P
a
rlak
tu
n
a
,
O.,
“
A
p
a
tt
e
rn
-
b
a
se
d
G
e
n
e
ti
c
A
l
g
o
rit
h
m
f
o
r
M
u
lt
i
-
R
o
b
o
t
C
o
v
e
ra
g
e
P
a
th
P
lan
n
in
g
M
in
im
izin
g
Co
m
p
letio
n
T
ime
”
,
J
o
u
rn
a
l
o
f
I
n
telli
g
e
n
t
M
a
n
u
fa
c
tu
rin
g
,
2
3
,
1
0
3
5
-
1
0
4
5
,
2
0
1
2
.
[
7
]
M
a
se
h
ian
,
E.
&
Am
in
-
Na
se
ri,
M
.
R.
,
“
A
V
o
ro
n
o
i
Dia
g
ra
m
-
V
isib
il
it
y
G
ra
p
h
-
P
o
ten
ti
a
l
F
iel
d
C
o
m
p
o
u
n
d
A
l
g
o
rit
h
m
f
o
r
Ro
b
o
t
P
a
th
P
lan
n
in
g
”
,
J
o
u
rn
a
l
o
f
Ro
b
o
t
ic S
y
ste
ms
,
2
1
,
2
7
5
-
3
0
0
,
2
0
0
4
.
[
8
]
Co
e
ll
o
,
C
o
e
ll
o
,
Ca
rl
o
s
A
.
,
Ga
r
y
L
a
m
o
n
t,
a
n
d
Da
v
id
V
a
n
V
e
ld
h
u
ize
n
,
“
Ev
o
lu
ti
o
n
a
ry
A
l
g
o
rit
h
m
s
f
o
r
S
o
lv
in
g
M
u
lt
i
-
Ob
jec
ti
v
e
P
r
o
b
lem
s”
,
G
e
n
e
ti
c
a
n
d
Ev
o
lu
ti
o
n
a
ry
Co
m
p
u
tati
o
n
S
e
ries
.
Ne
w
Yo
rk
:
S
p
rin
g
e
r
S
c
ien
c
e
&
Bu
sin
e
ss
M
e
d
ia L
L
C.
2
0
0
7
.
d
o
i:
1
0
.
1
0
0
7
/9
7
8
-
0
-
3
8
7
-
3
6
7
9
7
2
[
9
]
P
a
n
o
v
,
S
.
,
K
o
c
e
sk
i,
S.,
“
Ha
r
m
o
n
y
se
a
rc
h
b
a
se
d
a
l
g
o
rit
h
m
f
o
r
m
o
b
il
e
ro
b
o
t
g
lo
b
a
l
p
a
th
p
la
n
n
i
n
g
”
,
In
:
2
nd
M
e
d
it
e
rr
a
n
e
a
n
C
o
n
fer
e
n
c
e
o
n
E
mb
e
d
d
e
d
C
o
mp
u
ti
n
g
(
M
ECO)
,
15
-
2
0
J
u
n
e
2
0
1
3
,
p
p
.
1
6
8
-
1
7
1
,
2
0
1
3
.
[
1
0
]
M
,
R.
H.,
Hrin
g
,
S
c
h
il
li
n
g
,
H.,
S
c
h
,
B.
,
T
z
,
W
a
g
n
e
r,
D.
&
Wi
ll
h
a
lm
,
T
.
,
“
P
a
rti
ti
o
n
i
n
g
G
r
a
p
h
s
to
sp
e
e
d
u
p
Dijk
stra
'
s
A
l
g
o
rit
h
m
”
,
J
.
Exp
.
Al
g
o
rith
mic
s
,
1
1
,
2
.
8
,
2
0
0
7
.
[
1
1
]
Ko
c
e
sk
i,
S
.
,
P
a
n
o
v
,
S
.
,
Ko
c
e
sk
a
,
N.,
Zo
b
e
l,
P
.
B.
,
“
Du
ra
n
te,
F
.
:
A
No
v
e
l
Qu
a
d
Ha
r
m
o
n
y
S
e
a
rc
h
Alg
o
rit
h
m
f
o
r
G
rid
-
b
a
se
d
P
a
th
F
in
d
in
g
Re
g
u
lar
P
a
p
e
r”
,
In
t
J
Ad
v
R
o
b
o
t
S
y
st 1
1
.
2
0
1
4
,
d
o
i:
A
rtn
1
4
4
.
[
1
2
]
Zh
a
n
g
,
Z.
a
n
d
Z.
Z
h
a
o
,
“
A
M
u
lt
ip
le
M
o
b
il
e
R
o
b
o
ts
P
a
th
P
lan
n
in
g
A
lg
o
rit
h
m
B
a
se
d
o
n
a
-
S
tar
a
n
d
Dijk
stra
A
l
g
o
rit
h
m
”
,
In
ter
n
a
ti
o
n
a
l
J
o
u
rn
a
l
o
f
S
ma
rt H
o
me
,
8
(
3
):
7
5
-
86,
2
0
1
4
.
[
1
3
]
M
a
z
a
,
I.
,
Ollero
,
A
.
,
“
M
u
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