I
nte
rna
t
io
na
l J
o
urna
l o
f
E
lect
rica
l a
nd
Co
m
p
ute
r
E
ng
in
ee
ring
(
I
J
E
CE
)
Vo
l.
7
,
No
.
6
,
Dec
em
b
er
201
7
,
p
p
.
3
0
4
6
~
3
0
5
1
I
SS
N:
2
0
8
8
-
8708
,
DOI
: 1
0
.
1
1
5
9
1
/
i
j
ec
e
.
v7
i
6
.
p
p
3
0
4
6
-
3051
3046
J
o
ur
na
l ho
m
ep
a
g
e
:
h
ttp
:
//ia
e
s
jo
u
r
n
a
l.c
o
m/o
n
lin
e/in
d
ex
.
p
h
p
/I
JE
C
E
O
pti
m
a
l
Path
Pla
nning
u
sing
Equil
a
teral Spa
ces
O
ri
ented
Visibili
ty
G
ra
ph
M
ethod
No
r
B
a
da
riy
a
h Abdu
l La
t
ip
,
Ro
s
li O
m
a
r
,
Sa
njo
y
K
u
m
a
r
Debna
t
h
Un
iv
e
rsiti
T
u
n
Hu
ss
e
in
O
n
n
M
a
lay
sia
,
F
a
c
u
lt
y
o
f
El
e
c
tri
c
a
l
a
n
d
El
e
c
tro
n
ic E
n
g
in
e
e
rin
g
,
8
6
4
0
0
P
a
rit
Ra
ja,
Ba
tu
P
a
h
a
t,
J
o
h
o
r,
M
a
lay
sia
Art
icle
I
nfo
AB
ST
RAC
T
A
r
ticle
his
to
r
y:
R
ec
eiv
ed
Au
g
4
,
2
0
1
7
R
ev
i
s
ed
Oct
2
,
2
0
1
7
A
cc
ep
ted
Oct
1
6
,
2
0
1
7
P
a
t
h
p
lan
n
in
g
h
a
s
b
e
e
n
a
n
im
p
o
rtan
t
a
sp
e
c
t
in
th
e
d
e
v
e
l
o
p
m
e
n
t
o
f
a
u
to
n
o
m
o
u
s
c
a
rs
in
w
h
ich
p
a
t
h
p
lan
n
i
n
g
is
u
se
d
t
o
f
in
d
a
c
o
ll
isio
n
-
f
re
e
p
a
th
f
o
r
th
e
c
a
r
to
trav
e
rse
f
ro
m
a
sta
r
ti
n
g
p
o
i
n
t
S
p
to
a
targ
e
t
p
o
in
t
T
p
.
T
h
e
m
a
in
c
rit
e
ria
f
o
r
a
g
o
o
d
p
a
th
p
lan
n
in
g
a
lg
o
rit
h
m
in
c
lu
d
e
th
e
c
a
p
a
b
il
it
y
o
f
p
ro
d
u
c
in
g
th
e
s
h
o
rtes
t
p
a
th
w
it
h
a
lo
w
c
o
m
p
u
tatio
n
ti
m
e
.
L
o
w
c
o
m
p
u
tatio
n
ti
m
e
m
a
k
e
s
th
e
a
u
to
n
o
m
o
u
s
c
a
r
a
b
le
to
re
-
p
lan
a
n
e
w
c
o
ll
isio
n
-
f
re
e
p
a
th
to
a
v
o
id
a
c
c
id
e
n
t.
Ho
w
e
v
e
r,
th
e
m
a
in
p
ro
b
lem
w
it
h
m
o
st
p
a
th
p
lan
n
in
g
m
e
th
o
d
s
is
th
e
ir
c
o
m
p
u
tati
o
n
ti
m
e
in
c
re
a
se
s
a
s
th
e
n
u
m
b
e
r
o
f
o
b
st
a
c
les
in
th
e
e
n
v
iro
n
m
e
n
t
in
c
re
a
se
s.
In
th
is
p
a
p
e
r,
a
n
a
lg
o
rit
h
m
b
a
se
d
o
n
v
isib
i
li
ty
g
r
a
p
h
(V
G
)
is
p
ro
p
o
se
d
.
I
n
t
h
e
p
ro
p
o
se
d
a
lg
o
rit
h
m
,
w
h
ich
is
c
a
ll
e
d
Eq
u
il
a
tera
l
S
p
a
c
e
Orie
n
ted
V
isib
i
li
ty
G
ra
p
h
(E
S
O
V
G
),
th
e
n
u
m
b
e
r
o
f
o
b
sta
c
les
c
o
n
sid
e
re
d
f
o
r
p
a
t
h
p
lan
n
i
n
g
is
r
e
d
u
c
e
d
b
y
in
tro
d
u
c
i
n
g
a
sp
a
c
e
in
w
h
ich
th
e
o
b
sta
c
les
li
e
.
T
h
is
m
e
a
n
s
th
e
o
b
s
tac
les
lo
c
a
ted
o
u
tsid
e
t
h
e
sp
a
c
e
a
re
ig
n
o
re
d
f
o
r
p
a
th
p
lan
n
i
n
g
.
F
ro
m
si
m
u
latio
n
,
th
e
p
r
o
p
o
se
d
a
lg
o
rit
h
m
h
a
s
a
n
im
p
ro
v
e
m
e
n
t
ra
te
o
f
u
p
t
o
9
0
%
w
h
e
n
c
o
m
p
a
re
d
to
V
G
.
T
h
is
m
a
k
e
s
th
e
a
lg
o
rit
h
m
is
su
it
a
b
le
to
b
e
a
p
p
li
e
d
in
re
a
l
-
ti
m
e
a
n
d
w
il
l
g
re
a
tl
y
a
c
c
e
lera
te
th
e
d
e
v
e
lo
p
m
e
n
t
o
f
a
u
to
n
o
m
o
u
s ca
rs
in
t
h
e
n
e
a
r
f
u
tu
re
.
K
ey
w
o
r
d
:
Op
ti
m
al
p
ath
p
la
n
n
in
g
P
ath
p
lan
n
i
n
g
Vis
ib
ilit
y
g
r
ap
h
Co
p
y
rig
h
t
©
2
0
1
7
In
stit
u
te o
f
A
d
v
a
n
c
e
d
E
n
g
i
n
e
e
rin
g
a
n
d
S
c
ien
c
e
.
Al
l
rig
h
ts
re
se
rv
e
d
.
C
o
r
r
e
s
p
o
nd
ing
A
uth
o
r
:
R
o
s
li O
m
ar
,
Facu
lt
y
o
f
E
lectr
ical
&
E
lectr
o
n
ic
E
n
g
in
ee
r
i
n
g
,
Un
i
v
er
s
iti T
u
n
H
u
s
s
ei
n
On
n
Ma
la
y
s
ia,
8
6
4
0
0
P
a
r
it R
aj
a,
B
atu
P
ah
at,
J
o
h
o
r
,
Ma
lay
s
ia
.
E
m
ail:
r
o
s
lio
@
u
t
h
m
.
ed
u
.
m
y
1.
I
NT
RO
D
UCT
I
O
N
P
ath
p
lan
n
i
n
g
h
as
b
ee
n
an
em
er
g
i
n
g
tr
en
d
r
esear
ch
n
o
w
a
d
ay
s
to
ca
ter
th
e
n
ee
d
s
o
f
a
u
to
n
o
m
o
u
s
s
y
s
te
m
s
.
W
it
h
o
u
t
p
at
h
p
lan
n
i
n
g
,
a
u
to
n
o
m
o
u
s
ca
r
s
co
u
ld
n
o
t
b
e
m
ater
ialized
.
C
u
r
r
en
tl
y
,
t
h
er
e
ar
e
m
a
n
y
p
ath
p
lan
n
i
n
g
al
g
o
r
ith
m
s
th
at
h
a
v
e
b
ee
n
d
ev
elo
p
ed
b
ased
o
n
m
e
t
h
o
d
s
s
u
ch
as
Vo
r
o
n
o
i
d
ia
g
r
a
m
(
VD)
[
1
]
,
[
2
]
,
ce
ll
d
ec
o
m
p
o
s
itio
n
(
C
D)
[
3
]
,
[
4
]
,
g
en
et
ic
al
g
o
r
ith
m
(
G
A
)
[
5
]
,
[
6]
an
d
v
i
s
ib
ilit
y
g
r
ap
h
(
V
G)
,
to
n
a
m
e
a
f
e
w
.
A
p
ath
p
lan
n
in
g
al
g
o
r
ith
m
’
s
p
e
r
f
o
r
m
an
ce
is
n
o
r
m
all
y
m
ea
s
u
r
ed
b
ased
o
n
th
r
ee
cr
iter
ia
w
h
ic
h
in
c
lu
d
e
t
h
e
co
m
p
u
tatio
n
ti
m
e,
p
ath
o
p
ti
m
a
lit
y
a
n
d
co
m
p
leten
e
s
s
.
B
asicall
y
,
t
h
e
m
ain
co
n
s
tr
ai
n
t
th
at
a
f
f
ec
ts
t
h
e
co
m
p
u
tatio
n
ti
m
e
o
f
p
ath
p
lan
n
i
n
g
is
t
h
e
n
u
m
b
er
o
f
o
b
s
tacle
s
co
n
tain
ed
in
a
co
n
f
i
g
u
r
atio
n
s
p
ac
e
(
C
-
s
p
ac
e)
.
I
n
C
-
s
p
ac
e
s
ize
o
f
th
e
au
to
n
o
m
o
u
s
ca
r
is
r
e
d
u
ce
d
to
a
p
o
in
t
an
d
t
h
e
s
ize
o
f
o
b
s
tacle
is
e
n
lar
g
e
b
a
s
ed
o
n
t
h
e
s
ize
o
f
a
u
to
n
o
m
o
u
s
ca
r
[
7
]
.
T
h
e
h
i
g
h
er
t
h
e
n
u
m
b
er
o
f
o
b
s
tacle
s
in
C
-
s
p
ac
e,
th
e
h
ig
h
er
th
e
co
m
p
u
tat
io
n
ti
m
e
to
f
i
n
d
a
co
llis
io
n
-
f
r
ee
p
ath
.
An
o
p
ti
m
al
p
at
h
ca
n
b
e
d
ef
i
n
ed
as
th
e
s
h
o
r
test
p
ath
g
en
er
a
ted
b
y
a
p
ath
p
la
n
n
i
n
g
alg
o
r
it
h
m
a
m
o
n
g
all
t
h
e
p
r
o
d
u
ce
d
p
ath
co
m
m
en
cin
g
f
r
o
m
th
e
s
tar
ti
n
g
p
o
in
t
S
p
to
th
e
en
d
p
o
i
n
t
T
p
[
2
]
,
[
8
-
1
1
]
.
A
p
at
h
p
la
n
n
in
g
alg
o
r
ith
m
h
o
ld
s
th
e
co
m
p
lete
n
ess
cr
iter
io
n
i
f
it is
ab
le
to
p
r
o
d
u
ce
a
p
ath
if
o
n
e
e
x
is
ts
.
T
h
er
e
ar
e
s
ev
er
al
t
y
p
es
o
f
p
at
h
p
lan
n
i
n
g
s
u
ch
a
s
co
m
b
i
n
ato
r
ial
an
d
b
io
-
i
n
s
p
ir
ed
.
E
ac
h
o
f
th
e
m
h
as
th
eir
b
en
e
f
it
s
a
n
d
d
r
a
w
b
ac
k
s
i
n
s
ati
s
f
y
i
n
g
th
e
ab
o
v
e
-
m
en
t
io
n
ed
cr
iter
ia.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
J
E
C
E
I
SS
N:
2
0
8
8
-
8708
Op
tima
l P
a
th
P
la
n
n
in
g
u
s
in
g
E
q
u
ila
tera
l S
p
a
ce
s
Ori
en
ted
V
is
ib
ilit
y
….
(
N
o
r
B
a
d
a
r
iya
h
A
b
d
u
l La
tip
)
3047
VG
is
a
co
m
p
lete
al
g
o
r
ith
m
an
d
ca
p
ab
le
o
f
p
r
o
d
u
cin
g
a
p
ath
w
i
th
t
h
e
s
h
o
r
test
d
is
ta
n
ce
b
u
t
it
s
d
r
a
w
b
ac
k
i
s
th
e
co
m
p
u
tat
io
n
ti
m
e
i
n
cr
ea
s
e
s
w
h
e
n
t
h
e
n
u
m
b
er
o
f
o
b
s
tacle
s
in
C
-
s
p
ac
es
i
n
cr
ea
s
es
[
9
]
,
[
1
0
]
,
[
1
2
]
.
VD,
o
n
th
e
o
th
er
h
a
n
d
,
u
s
e
s
eq
u
id
is
ta
n
t
tec
h
n
iq
u
es
to
d
is
co
v
er
a
p
ath
f
o
r
w
h
ic
h
an
o
p
tim
a
l
p
ath
ca
n
n
o
t
b
e
ac
co
m
p
lis
h
ed
alt
h
o
u
g
h
it
h
as r
elativ
el
y
lo
w
er
co
m
p
u
tat
io
n
ti
m
e
an
d
th
e
al
g
o
r
ith
m
i
s
co
m
p
lete
[
2
]
,
[
1
3
]
.
CD
d
i
v
id
es
t
h
e
C
-
s
p
ac
es
i
n
to
ce
lls
w
h
ic
h
ar
e
d
i
s
cr
e
te
a
n
d
n
o
n
-
o
v
er
lap
p
in
g
.
A
p
ath
is
t
h
e
n
p
r
o
d
u
ce
d
th
r
o
u
g
h
a
ce
ll
t
h
at
i
s
n
o
t
o
cc
u
p
ied
b
y
t
h
e
o
b
s
tacle
.
T
h
e
d
r
a
w
b
ac
k
o
f
t
h
is
m
et
h
o
d
is
th
a
t
it
ca
n
n
o
t
p
r
o
d
u
ce
o
p
tim
a
l p
ath
alb
eit
it
h
as a
lo
w
er
co
m
p
u
tatio
n
t
i
m
e.
C
D
is
c
o
m
p
lete
w
h
er
e
it
f
in
d
s
a
p
ath
i
f
o
n
e
ex
is
t
s
.
T
h
e
b
io
-
in
s
p
ir
ed
t
y
p
e
g
et
s
m
o
t
iv
atio
n
f
r
o
m
n
at
u
r
e.
A
n
ap
p
lic
atio
n
o
f
o
p
er
ato
r
s
is
u
s
ed
in
t
h
e
Gen
etic
alg
o
r
ith
m
(
G
A
)
to
e
m
u
late
n
at
u
r
al
s
elec
tio
n
p
r
o
ce
s
s
.
T
h
e
d
is
ad
v
an
ta
g
e
o
f
G
A
is
t
h
at
t
h
er
e
ar
e
p
o
s
s
ib
ilit
ies
o
f
n
o
t
f
i
n
d
in
g
th
e
s
o
l
u
tio
n
in
n
ar
r
o
w
en
v
i
r
o
n
m
en
t
s
as
th
e
lo
ca
l
m
in
i
m
a
co
n
d
itio
n
s
m
a
y
o
cc
u
r
.
C
o
n
v
er
s
el
y
,
G
A
w
o
r
k
s
in
p
ar
allel
a
n
d
th
u
s
,
u
s
e
s
less
co
m
p
u
tat
io
n
ti
m
e.
I
t
is
m
eta
-
h
e
u
r
is
tics
a
n
d
h
e
n
ce
,
d
o
es
n
o
t
g
u
ar
a
n
tee
th
e
s
h
o
r
test
d
is
tan
ce
[
5
]
,
[
6
]
,
[
1
4
]
.
As
VG
is
ca
p
ab
le
o
f
p
r
o
d
u
cin
g
t
h
e
s
h
o
r
test
p
ath
an
d
co
m
p
l
ete,
in
th
i
s
p
ap
er
,
an
a
lg
o
r
it
h
m
b
ased
o
n
VG
is
p
r
o
p
o
s
ed
an
d
th
e
s
i
m
u
latio
n
is
p
er
f
o
r
m
ed
u
s
i
n
g
M
A
T
L
A
B
to
ev
al
u
ate
th
e
p
er
f
o
r
m
a
n
ce
o
f
t
h
e
p
r
o
p
o
s
ed
alg
o
r
ith
m
.
2.
P
AT
H
P
L
ANN
I
N
G
AL
G
O
R
I
T
H
M
T
h
e
p
r
o
p
o
s
ed
alg
o
r
ith
m
,
ca
lle
d
E
q
u
ilater
al
Sp
ac
e
Or
ie
n
ted
Vis
ib
ilit
y
Gr
ap
h
(
E
SOVG)
,
i
s
d
ep
icted
in
Fig
u
r
e
1
.
T
h
e
id
ea
o
f
th
e
al
g
o
r
ith
m
is
to
r
ed
u
ce
t
h
e
n
u
m
b
er
o
f
o
b
s
tacle
s
u
s
ed
w
h
e
n
p
la
n
n
i
n
g
a
p
ath
,
w
h
ic
h
w
il
l
in
t
u
r
n
,
le
s
s
e
n
th
e
co
m
p
u
t
atio
n
ti
m
e.
T
h
e
alg
o
r
ith
m
s
tar
ts
w
i
th
cr
ea
tin
g
a
b
ase
lin
e,
w
h
ic
h
co
n
n
e
cts
th
e
s
tar
tin
g
p
o
in
t
Sp
a
n
d
tar
g
et
p
o
in
t
T
p
.
T
h
e
o
p
en
in
g
a
n
g
le
i
s
t
h
en
s
et
to
n
o
m
in
a
l
v
alu
e
o
f
2
0
º
.
Af
ter
t
h
at,
a
m
id
-
p
o
i
n
t
b
et
w
ee
n
Sp
a
n
d
T
p
is
id
en
ti
f
ied
.
T
h
is
is
f
o
llo
w
ed
b
y
cr
ea
tin
g
a
m
id
-
l
in
e
w
h
ich
i
s
p
er
p
en
d
icu
lar
to
th
e
b
ase
lin
e
an
d
in
ter
s
ec
ti
n
g
th
e
m
id
-
p
o
i
n
t.
T
h
en
,
a
p
air
o
f
im
ag
in
ar
y
li
n
es
,
w
h
ich
e
m
er
g
e
f
r
o
m
Sp
to
w
ar
d
s
th
e
m
id
-
li
n
e
w
it
h
an
o
p
en
i
n
g
an
g
le
o
f
ar
e
cr
ea
ted
.
Si
m
ila
r
l
y
,
an
o
t
h
er
p
air
o
f
i
m
a
g
i
n
ar
y
lin
es
e
m
er
g
i
n
g
f
r
o
m
T
p
to
w
ar
d
s
th
e
m
id
-
lin
e
ar
e
d
r
a
w
n
.
B
o
t
h
p
air
s
o
f
li
n
es
s
h
o
u
ld
i
n
ter
s
ec
t
th
e
p
o
in
ts
d
en
o
te
d
b
y
C
1
a
n
d
C
2
.
T
h
e
eq
u
ila
ter
al
s
p
ac
e,
s
h
o
w
n
i
n
d
ar
k
er
co
lo
u
r
,
w
h
ic
h
is
f
o
r
m
e
d
b
y
f
o
u
r
i
m
a
g
i
n
ar
y
li
n
es
i
s
ill
u
s
tr
ated
in
Fig
u
r
e
2
.
A
l
g
o
r
i
t
h
m:
ESO
V
G
1:
C
r
e
a
t
e
a
b
a
se
l
i
n
e
c
o
n
n
e
c
t
i
n
g
s
t
a
r
t
i
n
g
p
o
i
n
t
S
p
a
n
d
t
a
r
g
e
t
p
o
i
n
t
T
p
2:
S
e
t
t
h
e
n
o
mi
n
a
l
o
p
e
n
i
n
g
a
n
g
l
e
t
o
2
0
º
3:
I
d
e
n
t
i
f
y
a
mi
d
-
p
o
i
n
t
o
n
t
h
e
b
a
se
l
i
n
e
b
e
t
w
e
e
n
S
p
a
n
d
T
p
4:
C
r
e
a
t
e
a
m
i
d
-
l
i
n
e
p
a
ssi
n
g
t
h
r
o
u
g
h
t
h
e
mi
d
-
p
o
i
n
t
a
n
d
p
e
r
p
e
n
d
i
c
u
l
a
r
t
o
t
h
e
b
a
se
l
i
n
e
5:
F
r
o
m e
a
c
h
S
p
a
n
d
T
p
,
c
r
e
a
t
e
a
p
a
i
r
o
f
i
mag
i
n
a
r
y
l
i
n
e
s w
i
t
h
a
n
o
p
e
n
i
n
g
a
n
g
l
e
o
f
ρ
t
o
w
a
r
d
s t
h
e
mi
d
l
i
n
e
.
6
:
C
r
e
a
t
e
a
n
e
q
u
i
l
a
t
e
r
a
l
sp
a
c
e
f
r
o
m t
h
e
e
n
c
l
o
se
d
a
r
e
a
b
y
t
h
e
f
o
u
r
i
m
a
g
i
n
a
r
y
l
i
n
e
s d
r
a
w
n
i
n
s
t
e
p
5
7:
I
d
e
n
t
i
f
y
t
h
e
o
b
st
a
c
l
e
s,
O
l
o
c
a
t
e
d
i
n
t
h
e
e
q
u
i
l
a
t
e
r
a
l
sp
a
c
e
8:
I
d
e
n
t
i
f
y
n
o
d
e
s l
i
st
o
f
t
h
e
o
b
st
a
c
l
e
i
n
st
e
p
7
9:
C
o
n
st
r
u
c
t
a
c
o
st
ma
t
r
i
x
b
a
se
d
o
n
t
h
e
n
o
d
e
s l
i
st
10:
F
i
n
d
t
h
e
s
h
o
r
t
e
st
p
a
t
h
f
r
o
m S
p
t
o
T
p
u
si
n
g
D
i
j
k
st
r
a
’
s
a
l
g
o
r
i
t
h
m
11:
I
f
n
o
p
a
t
h
i
s fo
u
n
d
w
i
t
h
i
n
t
h
e
e
q
u
i
l
a
t
e
r
a
l
sp
a
c
e
,
g
o
t
o
st
e
p
2
a
n
d
i
n
c
r
e
a
se
b
y
5
º
.
Fig
u
r
e
1
.
T
h
e
p
r
o
p
o
s
ed
alg
o
r
it
h
m
Fig
u
r
e
2
.
T
h
e
eq
u
ilater
al
s
p
ac
e
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8708
I
J
E
C
E
Vo
l.
7
,
No
.
6
,
Dec
em
b
er
2
0
1
7
:
3
0
4
6
–
3
0
5
1
3048
I
n
th
i
s
alg
o
r
it
h
m
,
t
h
e
s
ize
o
f
th
e
g
e
n
er
ated
eq
u
ilater
al
s
p
ac
e
d
ep
en
d
s
o
n
th
e
o
p
en
in
g
an
g
le
.
T
h
e
n
o
m
i
n
al
a
n
g
le
is
s
et
at
2
0
°
s
o
th
at
t
h
e
p
ath
p
la
n
n
in
g
ca
n
b
e
p
er
f
o
r
m
ed
w
i
th
a
lo
w
co
m
p
u
tatio
n
ti
m
e
as
t
h
e
n
u
m
b
er
o
f
o
b
s
tacle
s
co
n
ta
in
e
d
in
th
e
ar
ea
is
s
m
all.
Ho
w
e
v
er
,
if
a
p
ath
is
n
o
t
f
o
u
n
d
in
t
h
e
s
p
ac
e,
th
e
an
g
le
is
th
en
i
n
cr
ea
s
ed
b
y
5
º
u
n
til o
n
e
is
f
o
u
n
d
.
Fig
u
r
e
s
3
(
a)
-
Fi
g
u
r
e
3
(
d
)
s
h
o
w
th
e
s
i
m
u
la
tio
n
o
f
E
SO
VG
u
s
i
n
g
=
2
0
º
,
3
0
º
,
4
0
º
an
d
5
0
°,
r
esp
ec
tiv
el
y
.
T
h
e
f
i
g
u
r
e
s
s
h
o
w
1
0
0
o
b
s
tacle
s
p
r
ese
n
t
i
n
t
h
e
C
-
s
p
ac
e
s
b
u
t
o
n
l
y
a
f
e
w
o
b
s
tacle
s
(
s
h
o
w
n
i
n
g
r
ee
n
)
ar
e
b
ein
g
co
n
s
id
er
ed
b
y
E
SOVG
f
o
r
p
ath
p
lan
n
i
n
g
as
th
e
y
ar
e
f
u
ll
y
o
r
p
ar
tiall
y
co
n
ta
in
ed
i
n
t
h
e
eq
u
ilater
al
s
p
ac
e
(
en
clo
s
ed
b
y
th
e
s
o
lid
li
n
es).
On
t
h
e
o
th
er
h
an
d
,
t
h
e
o
b
s
tacle
s
s
h
o
w
n
in
w
h
ite
ar
e
i
g
n
o
r
ed
as
th
e
y
ar
e
to
tall
y
o
u
ts
id
e
th
e
s
p
ac
e
.
No
tice
th
at
th
e
s
m
aller
th
e
o
p
en
in
g
an
g
le
,
th
e
le
s
s
er
th
e
n
u
m
b
er
o
f
o
b
s
tacle
s
ar
e
tak
en
i
n
to
ac
co
u
n
t.
T
h
e
co
m
p
u
tatio
n
ti
m
e
a
n
d
p
ath
len
g
t
h
f
o
r
ea
ch
ar
e
s
h
o
w
n
i
n
T
ab
le
1
.
Fig
u
r
e
3
.
Si
m
u
latio
n
o
f
p
ath
p
lan
n
in
g
u
s
i
n
g
E
SOVG,
(
a)
ρ
=
2
0
°,
(
b
)
ρ
=3
0
°,
(
c)
ρ
=4
0
°,
(
d
)
ρ
=5
0
°
T
ab
le
1
.
C
o
m
p
u
ta
tio
n
ti
m
e
a
n
d
p
ath
len
g
t
h
u
s
i
n
g
d
if
f
er
en
t
v
alu
es o
f
C
o
mp
u
t
a
t
i
o
n
t
i
me
(
s)
P
a
t
h
l
e
n
g
t
h
(
u
n
i
t
)
2
0
º
0
.
4
3
8
9
6
4
5
.
6
7
5
3
0
º
0
.
7
1
4
5
6
4
5
.
6
7
5
4
0
º
1
.
5
4
7
8
6
4
5
.
6
7
5
50°
2
.
3
8
5
6
6
4
5
.
6
7
5
T
h
e
p
iece
w
is
e
li
n
ea
r
s
e
g
m
e
n
ts
s
h
o
w
n
in
d
ash
ed
l
in
e
ar
e
th
e
r
e
s
u
lt
in
g
p
at
h
o
f
E
SO
VG.
Fro
m
T
ab
le
1
,
it
is
o
b
s
er
v
ed
th
at
a
s
is
en
lar
g
ed
,
th
e
co
m
p
u
tatio
n
ti
m
e
i
n
cr
ea
s
es.
T
h
is
is
d
u
e
to
th
e
f
ac
t
t
h
at
t
h
e
n
u
m
b
er
o
f
o
b
s
tacle
s
co
n
tai
n
ed
in
th
e
eq
u
ila
ter
al
s
p
ac
e
i
s
i
n
cr
ea
s
ed
b
y
e
n
lar
g
in
g
.
Ho
w
e
v
e
r
,
th
e
p
at
h
le
n
g
th
s
ar
e
id
en
tical
w
it
h
d
if
f
er
en
t
.
T
h
u
s
,
it
is
i
m
p
o
r
tan
t
to
d
ec
id
e
th
e
v
al
u
e
o
f
th
a
t
ca
n
p
r
o
d
u
ce
an
o
p
tim
a
l
p
ath
w
h
ile
m
i
n
i
m
izi
n
g
t
h
e
co
m
p
u
ta
tio
n
ti
m
e.
3.
S
I
M
UL
AT
I
O
N
R
E
S
UL
T
S
T
h
e
ef
f
icie
n
c
y
o
f
th
e
al
g
o
r
it
h
m
ca
n
b
e
o
b
s
er
v
ed
th
r
o
u
g
h
a
s
i
m
u
latio
n
w
it
h
d
if
f
er
en
t
n
u
m
b
er
o
f
ob
s
tacle
s
,
i.e
.
b
et
w
ee
n
1
5
a
n
d
1
5
0
.
T
h
ese
n
u
m
b
er
s
o
f
o
b
s
ta
cles
r
ep
r
esen
t
d
i
f
f
er
en
t
d
e
n
s
i
t
y
o
f
th
e
o
b
s
tac
les
i
n
a
C
-
s
p
ac
e.
T
o
s
ee
th
e
ef
f
ec
t
o
f
th
e
o
p
en
i
n
g
a
n
g
le
,
th
r
ee
d
if
f
er
en
t
v
a
lu
e
s
ar
e
ap
p
lied
w
h
i
ch
ar
e
20°
,
3
0
º
an
d
Evaluation Warning : The document was created with Spire.PDF for Python.
I
J
E
C
E
I
SS
N:
2
0
8
8
-
8708
Op
tima
l P
a
th
P
la
n
n
in
g
u
s
in
g
E
q
u
ila
tera
l S
p
a
ce
s
Ori
en
ted
V
is
ib
ilit
y
….
(
N
o
r
B
a
d
a
r
iya
h
A
b
d
u
l La
tip
)
3049
40°
.
T
a
b
le
2
s
h
o
w
s
t
h
e
co
m
p
ar
is
o
n
o
f
co
m
p
u
ta
tio
n
t
i
m
e
to
s
ea
r
ch
t
h
e
co
lli
s
io
n
-
f
r
ee
p
ath
s
b
y
E
S
OVG
w
i
t
h
d
if
f
er
e
n
t
n
u
m
b
er
o
f
o
b
s
tacle
s
an
d
o
p
en
i
n
g
an
g
les
.
W
h
e
n
i
s
2
0
º
an
d
w
ith
1
5
o
b
s
tacle
s
in
th
e
s
ea
r
c
h
s
p
ac
e
,
th
e
co
m
p
u
tatio
n
ti
m
e
to
f
in
d
a
p
ath
is
0
.
0
6
7
9
s
.
A
s
i
s
en
l
ar
g
ed
to
3
0
°
an
d
4
0
°,
th
e
co
m
p
u
tat
io
n
ti
m
e
s
ar
e
0
.
0
9
5
5
s
an
d
0
.
1
2
5
1
s,
r
esp
ec
ti
v
el
y
.
W
h
en
th
e
n
u
m
b
er
o
f
o
b
s
tacle
s
is
in
cr
ea
s
ed
to
1
2
0
,
th
e
co
m
p
u
tatio
n
ti
m
e
at
=
2
0
°
is
0
.
6
1
5
6
s
.
W
ith
th
e
s
a
m
e
n
u
m
b
er
o
f
o
b
s
tacl
es
,
at
=
30°
an
d
=
40°
,
th
e
co
m
p
u
tatio
n
ti
m
es
ar
e
1
.
0
2
4
8
s
an
d
2
.
1
2
3
3
s
r
esp
ec
tiv
el
y
.
W
it
h
th
e
n
u
m
b
e
r
o
f
o
b
s
tacle
s
of
150
in
th
e
C
-
s
p
ac
e
,
at
=
2
0
°,
3
0
°
an
d
4
0
°,
th
e
co
m
p
u
tatio
n
ti
m
e
s
ar
e
1
.
0
3
3
4
s
,
1
.
6
6
6
2
s
an
d
3
.
0
2
7
5
s
,
r
esp
ec
tiv
el
y
.
T
h
e
s
i
m
u
latio
n
r
esu
lt
s
h
o
w
s
t
h
at
w
h
e
n
ρ
i
s
s
m
all,
w
h
ic
h
r
es
u
lt
s
i
n
s
m
al
l
eq
u
ila
t
er
al
s
p
ac
e
an
d
a
lo
w
n
u
m
b
er
o
f
o
b
s
tacle
s
,
t
h
e
co
m
p
u
tat
io
n
ti
m
e
is
lo
w
er
.
T
h
e
tr
en
d
o
f
th
e
co
m
p
u
tatio
n
ti
m
es
o
f
th
e
s
i
m
u
latio
n
is
d
ep
icted
in
Fi
g
u
r
e
4
.
T
h
e
p
er
f
o
r
m
a
n
ce
o
f
t
h
e
p
r
o
p
o
s
ed
alg
o
r
ith
m
i
s
ev
al
u
ated
b
y
co
m
p
ar
i
n
g
i
t
w
it
h
th
e
co
n
v
e
n
t
io
n
al
VG
m
et
h
o
d
.
T
ab
le
3
s
h
o
w
s
t
h
e
c
o
m
p
ar
is
o
n
o
f
co
m
p
u
tatio
n
ti
m
e
a
n
d
p
ath
len
g
t
h
b
et
w
ee
n
t
h
e
co
n
v
e
n
tio
n
al
VG
an
d
E
SOVG
w
it
h
=2
0
°.
T
ab
le
2
.
C
o
m
p
ar
is
o
n
o
f
co
m
p
u
tatio
n
t
i
m
e
s
w
it
h
d
if
f
er
en
t v
a
lu
es o
f
ρ
an
d
o
b
s
tacle
s
N
u
mb
e
r
o
f
o
b
s
t
a
c
l
e
s
C
o
mp
u
t
a
t
i
o
n
t
i
me
(
s)
a
t
ρ
=
2
0
°
C
o
mp
u
t
a
t
i
o
n
t
i
me
(
s)
a
t
ρ
=
3
0
°
C
o
mp
u
t
a
t
i
o
n
t
i
me
(
s)
a
t
ρ
=
4
0
°
15
0
.
0
6
7
9
0
.
0
9
5
5
0
.
1
2
5
1
30
0
.
1
3
1
1
0
.
1
8
0
8
0
.
2
7
5
3
45
0
.
1
5
9
9
0
.
1
9
9
2
0
.
3
6
8
3
60
0
.
2
4
3
9
0
.
2
9
0
3
0
.
5
3
7
1
75
0
.
3
5
4
1
0
.
4
6
0
2
0
.
8
7
9
8
90
0
.
3
8
8
0
0
.
5
6
0
7
1
.
2
4
1
8
1
0
5
0
.
4
5
4
5
0
.
7
3
2
4
1
.
5
7
6
1
1
2
0
0
.
6
1
5
6
1
.
0
2
4
8
2
.
1
2
3
3
1
5
0
1
.
0
3
3
4
1
.
6
6
6
2
3
.
0
2
7
5
Fig
u
r
e
4
.
Si
m
u
latio
n
r
esu
l
ts
o
f
E
SOVG
w
it
h
d
if
f
er
en
t
v
a
lu
es a
n
d
d
if
f
er
en
t
n
u
m
b
er
s
o
f
o
b
s
tacle
s
T
ab
le
3
.
P
er
f
o
r
m
a
n
ce
co
m
p
ar
i
s
o
n
b
et
w
ee
n
co
n
v
e
n
tio
n
a
l V
G
an
d
E
SOVG
C
o
n
v
e
n
t
i
o
n
a
l
V
G
ESO
V
G
w
i
t
h
ρ
=
2
0
°
N
o
o
f
o
b
st
a
c
l
e
s
C
o
mp
u
t
a
t
i
o
n
t
i
me
(
s)
P
a
t
h
l
e
n
g
t
h
(
u
n
i
t
)
C
o
mp
u
t
a
t
i
o
n
t
i
me
(
s)
P
a
t
h
l
e
n
g
t
h
(
u
n
i
t
)
I
mp
r
o
v
e
me
n
t
r
a
t
e
(
%)
15
0
.
1
8
6
2
5
2
0
.
1
3
4
3
0
.
0
6
7
9
5
2
0
.
1
3
4
3
6
3
.
5
30
0
.
6
5
2
3
5
2
0
.
1
3
4
2
0
.
1
3
1
1
5
2
0
.
1
3
4
3
7
9
.
9
45
1
.
4
5
6
8
6
2
7
.
4
4
9
2
0
.
1
5
9
9
6
2
7
.
4
4
9
2
8
9
.
0
60
2
.
2
3
5
7
6
3
1
.
0
0
2
4
0
.
2
4
3
9
6
3
1
.
0
0
2
4
8
9
.
0
75
3
.
2
0
8
7
6
4
5
.
6
7
5
0
0
.
3
5
4
1
6
4
5
.
6
7
5
0
8
9
.
0
90
4
.
4
1
4
3
6
4
5
.
6
7
5
0
0
.
3
8
8
0
6
4
5
.
6
7
5
0
9
1
.
2
1
0
5
6
.
0
4
6
5
6
4
5
.
6
7
5
0
0
.
4
5
4
5
6
4
5
.
6
7
5
0
9
2
.
5
1
2
0
7
.
3
8
7
3
7
5
0
.
2
2
5
8
0
.
6
1
5
6
7
5
0
.
2
2
5
8
9
2
.
0
1
5
0
1
1
.
8
6
7
1
9
1
0
.
8
7
2
3
1
.
0
3
3
4
9
1
0
.
8
7
2
3
9
1
.
3
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8708
I
J
E
C
E
Vo
l.
7
,
No
.
6
,
Dec
em
b
er
2
0
1
7
:
3
0
4
6
–
3
0
5
1
3050
W
h
en
t
h
e
n
u
m
b
er
o
f
o
b
s
tacle
s
is
1
5
,
th
e
co
m
p
u
ta
tio
n
t
i
m
e
f
o
r
f
i
n
d
in
g
a
p
at
h
b
y
co
n
v
e
n
t
io
n
al
VG
i
s
0
.
1
8
6
2
s
,
an
d
b
y
E
SOVG
is
0
.
0
6
7
9
s
.
E
SOVG
i
m
p
r
o
v
es
th
e
co
m
p
u
tatio
n
ti
m
e
by
u
p
to
6
3
.
5
%.
W
h
en
th
e
n
u
m
b
er
o
f
t
h
e
o
b
s
tacle
i
s
i
n
c
r
ea
s
ed
to
7
5
,
th
e
co
m
p
u
tatio
n
ti
m
e
o
f
co
n
v
e
n
tio
n
al
V
G
is
3
.
2
0
8
7
s
w
h
ile
t
h
e
E
SOVG
’
s
i
s
0
.
3
5
4
1
s
,
w
h
ic
h
r
ed
u
ce
s
th
e
VG
co
m
p
u
tati
o
n
ti
m
e
b
y
89
%.
W
ith
1
5
0
o
b
s
tacle
s
in
t
h
e
en
v
ir
o
n
m
e
n
t
,
t
h
e
co
m
p
u
tatio
n
ti
m
e
b
y
co
n
v
e
n
tio
n
a
l
VG
an
d
E
SOVG
ar
e
1
1
.
8
6
7
1
s
an
d
1
.
0
3
3
4
s
r
esp
ec
tiv
el
y
.
T
h
e
im
p
r
o
v
e
m
e
n
t r
ec
o
r
d
ed
b
y
E
SOVG
is
9
1
.
3
%.
Fig
u
r
e
5
clea
r
ly
s
h
o
w
s
t
h
e
tr
e
n
d
s
o
f
co
m
p
u
tatio
n
ti
m
e
s
of
co
n
v
e
n
tio
n
al
V
G
an
d
E
SOVG.
T
h
e
tr
en
d
in
d
icate
s
t
h
at
w
h
e
n
th
e
n
u
m
b
er
o
f
o
b
s
tacle
s
escalate
s
,
th
e
co
m
p
u
tatio
n
ti
m
e
of
co
n
v
e
n
ti
o
n
al
VG
i
n
cr
ea
s
e
s
ex
p
o
n
en
t
iall
y
,
w
h
er
ea
s
E
S
OV
G
h
as
co
m
p
u
tatio
n
ti
m
e
th
at
r
i
s
es
al
m
o
s
t li
n
ea
r
l
y
.
I
n
ter
m
s
o
f
p
ath
le
n
g
th
,
f
r
o
m
T
ab
le
3
,
it
ca
n
b
e
s
ee
n
th
at
b
o
th
VG
an
d
E
SOV
G
p
r
o
d
u
ce
p
ath
w
ith
id
en
tical
le
n
g
th
s
.
T
h
is
p
r
o
v
es
th
at
w
h
ile
E
SO
VG
r
ed
u
ce
s
t
h
e
co
m
p
u
tatio
n
ti
m
e
,
it
m
a
in
ta
i
n
s
t
h
e
o
p
ti
m
al
it
y
o
f
th
e
r
esu
lti
n
g
p
at
h
in
ter
m
s
o
f
l
en
g
t
h
.
Fig
u
r
e
5
.
C
o
m
p
ar
is
o
n
VG
a
n
d
E
q
u
ilater
al
s
p
ac
es VG
4.
CO
NCLU
SI
O
N
E
q
u
ilater
al
S
p
ac
es
Or
ien
ted
Vis
ib
ilit
y
Gr
ap
h
(
E
SOVG)
al
g
o
r
ith
m
h
as
b
ee
n
p
r
o
p
o
s
ed
in
th
is
p
ap
er
to
o
v
er
co
m
e
a
h
i
g
h
co
m
p
u
ta
tio
n
ti
m
e
in
co
n
v
e
n
tio
n
al
VG.
E
SOVG
cr
ea
te
s
a
n
e
q
u
ilater
al
s
p
ac
e
u
s
i
n
g
f
o
u
r
i
m
a
g
in
ar
y
li
n
es
to
r
ed
u
ce
t
h
e
co
n
s
id
er
ed
n
u
m
b
er
o
f
o
b
s
tac
les
w
h
e
n
p
lan
n
i
n
g
a
co
lli
s
io
n
-
f
r
ee
p
at
h
.
I
n
E
SOVG,
t
h
e
s
ize
o
f
t
h
e
eq
u
ila
ter
al
s
p
ac
e
is
d
eter
m
i
n
ed
b
y
th
e
o
p
en
in
g
a
n
g
le
.
T
h
e
n
o
m
i
n
al
v
al
u
e
o
f
i
s
s
e
t
to
2
0
º
an
d
if
a
co
llis
io
n
-
f
r
ee
p
ath
co
u
ld
n
o
t
b
e
f
o
u
n
d
i
n
t
h
e
s
p
ac
e,
it
w
ill
b
e
i
n
cr
ea
s
ed
g
r
ad
u
all
y
u
n
til
a
p
at
h
is
f
o
u
n
d
i
n
t
h
e
s
p
ac
e.
E
SOVG
h
as
b
ee
n
co
m
p
ar
ed
w
it
h
t
h
e
co
n
v
e
n
tio
n
al
V
G
i
n
ter
m
s
o
f
c
o
m
p
u
tat
io
n
ti
m
e
a
n
d
p
ath
len
g
t
h
.
I
t
w
as
f
o
u
n
d
t
h
at
E
SOVG
w
a
s
ab
le
to
i
m
p
r
o
v
e
t
h
e
co
m
p
u
tatio
n
ti
m
e
d
r
asti
ca
ll
y
i
n
co
m
p
ar
is
o
n
w
it
h
t
h
e
co
n
v
en
tio
n
al
VG
w
h
i
le
th
e
opt
i
m
ali
t
y
o
f
th
e
p
at
h
le
n
g
t
h
w
a
s
m
ai
n
tai
n
ed
.
I
n
t
h
e
f
u
tu
r
e
E
SOVG
c
o
u
ld
b
e
test
ed
in
a
d
y
n
a
m
ic
e
n
v
ir
o
n
m
e
n
t
w
h
ic
h
h
as
m
o
v
i
n
g
a
n
d
p
o
p
-
u
p
o
b
s
tacle
s
.
I
f
E
SO
VG
w
er
e
s
u
cc
es
s
i
n
d
y
n
a
m
ic
e
n
v
ir
o
n
m
en
t,
it
co
u
l
d
b
e
ap
p
lied
in
a
u
to
n
o
m
o
u
s
ca
r
,
in
w
h
ic
h
a
lo
w
er
co
m
p
u
tati
o
n
ti
m
e
is
n
ec
es
s
ar
y
in
o
r
d
er
to
b
e
ap
p
lied
in
r
ea
l ti
m
e.
ACK
NO
WL
E
D
G
E
M
E
NT
S
T
h
is
w
o
r
k
is
s
u
p
p
o
r
ted
b
y
U
n
iv
er
s
iti
T
u
n
H
u
s
s
ei
n
O
n
n
Ma
l
a
y
s
ia
(
UT
HM
)
an
d
f
u
n
d
ed
b
y
Vo
t
1
4
8
9
o
f
Fu
n
d
a
m
e
n
tal
R
esear
c
h
Gr
an
t Sc
h
e
m
e
(
F
R
G
S
).
RE
F
E
R
E
NC
E
S
[1
]
X
.
C
h
e
n
a
n
d
X.
Ch
e
n
,
“
T
h
e
UA
V
d
y
n
a
mic
p
a
th
p
l
a
n
n
i
n
g
a
l
g
o
rit
h
m
re
se
a
rc
h
e
d
b
a
se
d
o
n
Vo
ro
n
o
i
d
i
a
g
ra
m
,
”
i
n
2
0
1
4
2
6
t
h
C
h
in
e
se
Co
n
tro
l
a
n
d
D
e
c
isio
n
Co
n
f
e
re
n
c
e
(CCDC)
,
2
0
1
4
,
n
o
.
6
1
0
7
4
1
5
9
,
p
p
.
1
0
6
9
–
1
0
7
1
.
[2
]
C.
P
e
n
g
,
X
.
L
u
,
J.
Da
i,
a
n
d
L
.
Yi
n
,
“
Re
se
a
rc
h
o
f
P
a
th
P
lan
n
in
g
M
e
th
o
d
Ba
se
d
On
th
e
Im
p
ro
v
e
d
V
o
ro
n
o
i
Dia
g
ra
m
,
”
2
0
1
3
,
p
p
.
2
9
4
0
–
2
9
4
4
.
[3
]
M
.
Klo
e
tze
r,
“
Op
t
im
izin
g
Ce
ll
De
c
o
m
p
o
siti
o
n
P
a
t
h
P
lan
n
i
n
g
f
o
r
M
o
b
i
le
Ro
b
o
ts
u
sin
g
Dif
f
e
re
n
t
M
e
tri
c
s,”
IEE
E
,
Evaluation Warning : The document was created with Spire.PDF for Python.
I
J
E
C
E
I
SS
N:
2
0
8
8
-
8708
Op
tima
l P
a
th
P
la
n
n
in
g
u
s
in
g
E
q
u
ila
tera
l S
p
a
ce
s
Ori
en
ted
V
is
ib
ilit
y
….
(
N
o
r
B
a
d
a
r
iya
h
A
b
d
u
l La
tip
)
3051
p
p
.
5
6
5
–
5
7
0
,
2
0
1
5
.
[4
]
D.
H.
Ki
m
e
t
a
l.
,
“
P
a
th
T
ra
c
k
in
g
Co
n
tro
l
Co
v
e
ra
g
e
o
f
a
M
in
in
g
Ro
b
o
t
Ba
se
d
o
n
Ex
h
a
u
stiv
e
P
a
t
h
P
la
n
n
i
n
g
w
it
h
Ex
a
c
t
Ce
ll
De
c
o
m
p
o
siti
o
n
,
”
2
0
1
4
,
p
p
.
7
3
0
–
7
3
5
.
[5
]
S
.
A
in
a
ss
e
r,
A
.
I
m
a
m
,
H.
Be
n
n
a
c
e
u
r,
a
n
d
A
.
Im
a
m
,
“
A
n
e
ff
icie
n
t
G
e
n
e
ti
c
A
l
g
o
rit
h
m
f
o
r
th
e
G
lo
b
a
l
Ro
b
o
t
P
a
t
h
P
la
n
n
i
n
g
P
ro
b
lem
,
”
p
p
.
9
7
–
1
0
2
,
2
0
1
6
.
[6
]
Z.
Yo
n
g
n
ia
n
,
Z.
L
if
a
n
g
,
a
n
d
L
.
Yo
n
g
p
in
g
,
“
A
n
Im
p
ro
v
e
d
Ge
n
e
ti
c
A
l
g
o
rit
h
m
f
o
r
M
o
b
il
e
R
o
b
o
ti
c
P
a
t
h
P
lan
n
i
n
g
,
”
2
0
1
2
.
[7
]
L
.
L
u
lu
a
n
d
A
.
El
n
a
g
a
r,
“
A
c
o
m
p
a
ra
ti
v
e
stu
d
y
b
e
t
w
e
e
n
v
isib
il
it
y
-
b
a
se
d
ro
a
d
m
a
p
p
a
th
p
la
n
n
i
n
g
a
lg
o
rit
h
m
s,”
In
tell.
Ro
b
o
t.
S
y
st.
2
0
0
5
.
(
IROS
,
2
0
0
5
.
[8
]
M
.
S
.
G
a
n
e
sh
m
u
rth
y
a
n
d
G
.
.
S
u
re
sh
,
“
P
a
t
h
P
lan
n
i
n
g
A
lg
o
rit
h
m
f
o
r
A
u
to
n
o
m
o
u
s
M
o
b
il
e
R
o
b
o
t
i
n
Dy
n
a
m
ic
En
v
iro
n
m
e
n
t,
”
2
0
1
5
,
v
o
l.
1
5
,
p
p
.
1
–
6.
[9
]
T
.
Ng
u
y
e
t,
N.
Du
y
-
T
u
n
g
,
V
.
Du
c
-
L
u
n
g
,
a
n
d
T
.
Ng
u
y
e
n
-
V
u
,
“
G
lo
b
a
l
P
a
t
h
P
lan
n
i
n
g
f
o
r
A
u
to
n
o
m
o
u
s
Ro
b
o
ts
u
si
n
g
M
o
d
if
ied
V
isi
b
il
it
y
g
ra
p
h
,
”
IEE
E
,
v
o
l.
1
3
,
p
p
.
3
1
7
–
3
2
1
,
2
0
1
3
.
[1
0
]
T
.
T
.
Nh
u
Ng
u
y
e
t,
T
.
V
a
n
Ho
a
i,
a
n
d
N.
A
.
T
h
i,
“
S
o
m
e
a
d
v
a
n
c
e
d
tec
h
n
i
q
u
e
s
in
re
d
u
c
in
g
ti
m
e
f
o
r
p
a
th
p
la
n
n
i
n
g
b
a
se
d
o
n
v
isib
il
it
y
g
ra
p
h
,
”
2
0
1
1
,
p
p
.
1
9
0
–
1
9
4
.
[1
1
]
B.
S
icili
a
n
o
,
L
.
S
c
iav
icc
o
,
L
.
Vill
a
n
i,
a
n
d
G
.
Orio
lo
,
Ro
b
o
ti
c
s M
o
d
e
ll
in
g
Pl
a
n
n
i
n
g
a
n
d
C
o
n
tro
l
.
S
p
ri
n
g
e
r,
2
0
0
9
.
[1
2
]
M
.
Yin
g
c
h
o
n
g
,
Z.
G
a
n
g
,
a
n
d
P
.
W
il
f
rid
,
“
Co
o
p
e
ra
ti
v
e
p
a
th
p
la
n
n
in
g
f
o
r
m
o
b
il
e
ro
b
o
ts
b
a
se
d
o
n
v
isib
il
it
y
g
ra
p
h
,
”
2
0
1
3
,
p
p
.
4
9
1
5
–
4
9
2
0
.
[1
3
]
R.
G
o
n
z
a
lez
,
C.
M
a
h
u
lea
,
a
n
d
M
.
Klo
e
tze
r,
“
A
M
a
tl
a
b
-
b
a
se
d
In
tera
c
ti
v
e
S
i
m
u
lato
r
f
o
r
T
e
a
c
h
in
g
M
o
b
il
e
R
o
b
o
ti
c
s,”
p
p
.
1
–
2
0
,
S
e
p
.
2
0
1
4
.
[1
4
]
N.
A
c
h
o
u
r,
“
M
o
b
il
e
Ro
b
o
ts
Pa
t
h
Pl
a
n
n
in
g
u
si
n
g
Ge
n
e
ti
c
Al
g
o
r
it
h
ms
,
”
ICA
S
2
0
1
1
S
e
v
e
n
th
I
n
t.
Co
n
f
.
A
u
to
n
.
A
u
to
n
.
S
y
st.
,
n
o
.
c
,
p
p
.
1
1
1
–
1
1
5
,
2
0
1
1
.
B
I
O
G
RAP
H
I
E
S
O
F
AUTH
O
RS
No
r
B
a
d
a
r
iy
a
h
Abd
u
l
La
ti
p
,
re
c
e
iv
e
d
h
e
r
b
a
c
h
e
lo
r
in
El
e
c
tro
n
ic
En
g
in
e
e
rin
g
f
ro
m
Un
iv
e
rsiti
T
u
n
Hu
ss
e
in
O
n
n
M
a
lay
sia
(U
THM
)
in
2
0
1
5
.
He
r
re
se
a
rc
h
in
tere
sts
a
re
in
r
o
b
o
ti
c
p
a
th
p
la
n
n
i
n
g
,
c
o
n
tro
l
sy
ste
m
a
n
d
m
e
d
ica
l
e
lec
tro
n
ic.
Up
o
n
g
ra
d
u
a
t
io
n
sh
e
p
u
r
su
e
s
h
is
m
a
ste
r
in
El
e
c
tri
c
a
l
En
g
in
e
e
rin
g
in
UT
HM.
S
h
e
is
c
u
r
re
n
tl
y
a
M
a
ste
r
d
e
g
re
e
stu
d
e
n
t
a
t
UT
HM.
Ro
sli
O
m
a
r
is
a
se
n
io
r
lec
tu
re
r
a
t
F
a
c
u
lt
y
o
f
El
e
c
tri
c
a
l
&
El
e
c
tro
n
ic E
n
g
in
e
e
rin
g
,
Un
iv
e
rsiti
T
u
n
Hu
ss
e
in
On
n
M
a
lay
sia
.
He
re
c
e
iv
e
d
h
is
P
h
D i
n
e
n
g
in
e
e
rin
g
f
ro
m
U
n
iv
e
rsity
o
f
Leic
e
ste
r,
Un
it
e
d
Kin
g
d
o
m
in
2
0
1
2
.
His res
e
a
rc
h
in
tere
sts a
re
in
ro
b
o
ti
c
e
n
g
in
e
e
rin
g
,
a
u
to
n
o
m
o
u
s sy
ste
m
a
n
d
s
y
ste
m
id
e
n
ti
f
ica
ti
o
n
.
S
a
n
jo
y
K
u
m
a
r
De
b
n
a
t
h
is
a
2
n
d
-
y
e
a
r
P
h
D
stu
d
e
n
t
in
t
h
e
De
p
a
rtme
n
t
o
f
M
e
c
h
a
tro
n
ic
a
n
d
R
o
b
o
ti
c
e
n
g
in
e
e
rin
g
,
Facu
lt
y
o
f
E
lectr
ical
&
E
lectr
o
n
ic
E
n
g
i
n
ee
r
in
g
in
th
e
Un
iv
e
rsiti
T
u
n
Hu
ss
e
in
On
n
M
a
lay
sia
(UTHM
)
.
He
re
c
e
iv
e
d
h
is
M
a
ste
rs
o
f
En
g
in
e
e
rin
g
f
ro
m
Un
iv
e
rsit
i
T
e
k
n
o
lo
g
i
M
a
lay
sia
in
2
0
1
4
a
n
d
B
a
c
h
e
lo
r
o
f
E
n
g
in
e
e
rin
g
De
g
re
e
f
ro
m
th
e
P
re
sid
e
n
c
y
Un
iv
e
rsit
y
Ba
n
g
lad
e
sh
in
2
0
0
8
.
He
jo
in
e
d
a
re
se
a
rc
h
o
n
“
Op
ti
m
a
l
En
e
rg
y
E
ff
icie
n
t
P
a
t
h
P
la
n
n
i
n
g
f
o
r
a
n
Un
m
a
n
n
e
d
A
ir
V
e
h
icle
(UA
V
)
in
Ob
sta
c
le
-
Ri
c
h
En
v
iro
n
m
e
n
t”
in
2
0
1
5
a
t
UT
HM
w
it
h
re
s
e
a
rc
h
G
ra
n
t
u
n
d
e
r
th
e
Off
ic
e
f
o
r
Re
s
e
a
rc
h
,
In
n
o
v
a
ti
o
n
,
Co
m
m
e
r
c
ializa
ti
o
n
,
a
n
d
Co
n
su
l
tan
c
y
M
a
n
a
g
e
m
e
n
t
(ORICC).
Evaluation Warning : The document was created with Spire.PDF for Python.