ARO优化：移动机器人编队路径规划与控制
需积分: 9 157 浏览量
更新于2024-09-09
收藏 3.57MB PDF 举报
本文主要探讨了在存在障碍物的情况下，采用Asexual Reproduction Optimization (ARO)方法对非holonomic多机器人系统中的领导者-跟随者结构进行最优路径规划与同步形成控制的问题。作者们针对移动机器人的路径优化问题，提出了一个创新的解决方案，将Asexual Reproduction Optimization算法应用于控制领域，以解决复杂环境中的路径规划问题。
ARO是一种软计算技术，它模仿生物界的无性生殖过程，通过模拟自然选择、遗传变异和遗传操作等机制来搜索解空间，从而找到最优解。在这个研究中，ARO被用来寻找在多机器人系统中，如何使领导者机器人引领其他跟随者机器人形成有序的队形，并同时避开环境中的障碍物，以实现高效、安全的导航。
为了提高算法的性能，文章还提到了与其他优化方法的比较，如Simulated Annealing（模拟退火）和Improved Harmony Search and Cuckoo Optimization Algorithm (COA)。这些传统优化算法在此背景下作为基准，用来评估ARO在解决路径规划问题时的效率和效果。通过对比实验，作者可能展示了ARO在处理动态环境和约束条件下的优势，比如路径的平滑度、实时性和稳定性。
路径规划部分的关键知识点包括：
1. **非holonomic约束**：非holonomic机器人受到物理限制，其运动轨迹不能由当前位置和速度完全确定，需要考虑加速度或更高阶的运动学特性，这在路径规划中增加了复杂性。
2. **领导-跟随者结构**：这种组织方式允许机器人系统中的一部分负责引导，而其他机器人则按照预定规则跟随，提高了整体的协作效率和灵活性。
3. **人工势场法**：作为一种经典的路径规划策略，人工势场用于构建虚拟力场，引导机器人沿最小阻力路径移动，有助于避开障碍。
4. **优化算法的选择与比较**：通过ARO与其他优化算法的对比，强调了ARO在适应性、收敛速度和全局优化能力上的特点，这对于实际应用中的路径规划决策至关重要。
总结来说，这篇文章提供了对多机器人路径规划和队形控制的一种新颖且有效的策略，利用了Asexual Reproduction Optimization来解决复杂环境中的路径优化问题，对于理解和推动未来无人系统在协作和自主导航方面的研究具有重要意义。
A.
Noormohammadi
Asl
et
al.
/
Applied
Soft
Computing
14
(2014)
563–576
565
function
that
may
possess
several
local
minima.
SA
is
able
to
deal
with
cost
functions
with
quite
arbitrary
degrees
of
nonlinearities,
discontinuities
and
stochasticity
and
can
process
quite
arbitrary
boundary
conditions
and
constraints
imposed
on
them.
It
has
been
shown
[50]
that
SA
is
a
quick
method
to
implement
in
real
time
applications.
The
method
works
by
imitating
the
physical
process
whereby
a
heated
solid
is
slowly
cooled
so
that
when
eventually
its
structure
is
frozen,
which
happens
at
a
minimum
energy
con-
figuration.
The
slow
cooling
gives
atoms
more
chances
of
finding
configurations
with
lower
internal
energy
than
the
initial
ones.
At
first,
a
random
initial
placement
is
selected
and
the
temperature
should
be
initialized
very
high.
The
next
step
is
perturbing
the
initial
placement
randomly
in
its
neighbourhood.
The
algorithm
determines
whether
the
new
point
is
better
or
worse
than
the
cur-
rent
point
by
evaluating
cost
function.
If
the
new
point
is
better
than
the
current
point,
it
becomes
the
next
point.
If
the
new
point
is
worse
than
the
current
point,
the
algorithm
decides
whether
to
accept
it
or
not
according
to
the
probabilistic
function
that
will
be
defined
later.
The
temperature
is
updated
by
lowering
it
according
to
a
deterministic
“cooling
schedule”.
The
probabilistic
function
is
defined
as
below:
P
=
e
((J
o
−J
n
)/T)
(1)
where
J
o
is
the
value
of
cost
function
in
the
previous
step,
J
n
is
the
value
of
the
cost
function
in
the
next
step,
is
the
probability
factor,
and
T
is
the
temperature
parameter.
If
the
temperature
is
too
high,
then
the
value
of
the
probabilistic
function
is
almost
“1”
which
means
in
the
primary
steps,
every
move
is
acceptable
even
if
the
cost
function
increases
(decreases).
This
feature
helps
in
the
way
that
the
algorithm
does
not
stick
in
local
minima
(maxima)
and
leads
to
the
global
minimum
(maximum).
By
decreasing
the
temperature,
the
value
of
the
probabilistic
function
decreases.
This
means
the
probability
of
having
a
move
that
increases
(decreases)
the
cost
function,
decreases.
Just
like
the
nature,
in
high
temper-
ature,
it’s
more
often
to
go
to
any
position
by
atoms.
However,
in
low
temperature,
they
are
willing
to
go
to
positions
in
which
they
can
lose
energy;
so
these
well-organized
structures
are
made.
2.2.
Improved
Harmony
Search
(IHS)
Harmony
Search
(HS)
is
inspired
from
improvisation
process
of
music
players
and
proposed
by
Geem
et
al.
[41].
Each
musician
improvisation
process
consists
of
three
possible
choices:
(i)
playing
any
famous
note
from
his
memory;
(ii)
playing
a
note
in
vicinity
of
previous
selected
note;
(iii)
selecting
a
note
randomly.
These
three
choices
are
the
basic
rules
of
HS
which
are
applied
in
algorithm,
by
harmony
memory
considering
rate
(HMCR)
and
pitch
adjusting
rate
(PAR)
parameters.
The
HMCR
is
the
probability
of
choosing
a
note
from
the
Harmony
Memory
(HM)
and
PAR,
is
the
probability
of
playing
a
note
in
vicinity
of
previous
selected
one.
HS
initializes
a
population
of
solution
vectors
randomly
and
saved
them
in
HM.
Then
generates
a
new
harmony
vector
using
the
rules.
If
new
vector,
overcomes
the
worst
harmony
in
the
HM,
according
to
a
fitness
function,
the
worst
harmony
vector
is
replaced
by
the
new
one.
For
generating
a
new
solution
vector
the
algorithm
may
select
value
of
each
decision
variable
either
from
HM,
with
the
probability
of
HMCR
or
randomly,
with
the
probability
of
(1-HMCR).
Then
with
the
probability
of
PAR,
decision
variables
which
are
chosen
from
HM
are
selected
for
pitch-adjusting.
In
pitch-adjustment
the
deci-
sion
variable
x
i
is
replaced
as
follow
x
i
←
x
i
±
r
and
×
bw
where
bw
is
an
arbitrary
distance
bandwidth.
HS
is
good
at
identifying
the
high
performance
regions
of
the
solution
space
at
a
reasonable
time,
but
it
cannot
make
an
effective
balance
between
global
and
local
search
and
gets
into
trouble
in
performing
local
search
for
numerical
applications.
PAR
parameter
plays
a
very
important
role
in
fine-tuning
of
optimized
solution
vectors,
and
it
is
effective
in
adjusting
convergence
rate
to
optimal
solution.
Therefore
[42]
proposed
Improved
Harmony
Search
(IHS)
algorithm
which
uses
dynamic
increasing
PAR
param-
eter,
and
decreasing
the
bandwidth
(bw)
instead
of
fixed
values,
in
order
to
solve
the
mentioned
problem.
PAR
and
bw
change
dynam-
ically
with
generation
number
as
follow:
PAR(gn)
=
PAR
min
+
PAR
max
−
PAR
min
NI
×
gn
(2)
where
PAR(gn)
is
pitch
adjusting
rate
for
each
generation,
PAR
min
and
PAR
max
is
minimum
and
maximum
pitch
adjusting
rate
respec-
tively,
NI
is
number
of
solution
vector
generations
and
gn
is
generation
number.
bw(gn)
−
bw
max
exp(c
·
gn),
c
=
ln(bw
min
/bw
max
)
NI
(3)
where
bw(gn)
is
bandwidth
for
each
generation,
bw
min
and
bw
max
is
minimum
and
maximum
bandwidth
respectively.
2.3.
Cuckoo
Optimization
Algorithm
(COA)
Cuckoo
Optimization
Algorithm
(COA),
an
optimization
algo-
rithm
which
is
inspired
by
lifestyle
of
a
bird
called
Cuckoo.
Special
characteristics
of
this
bird
in
egg
laying
and
breeding
had
been
the
basic
motivation
for
development
of
this
algorithm.
Each
individual
in
the
algorithm
has
a
habitat
around
which
she
starts
to
lay
eggs
[43].
Finding
the
optimal
solution
and
fast
convergence
are
some
merits
of
COA.
COA
starts
with
an
initial
population
of
cuckoos.
In
COA,
decision
variables
are
formed
as
an
array
(1
×
N
var
),
which
is
called
habitat
and
show
current
living
position
of
cuckoo.
The
initial
population
is
a
candidate
habitat
matrix
of
size
N
p
×
N
var
where
N
p
is
population
size.
This
population
laid
their
eggs
in
the
nest
of
other
kind
of
birds.
Each
cuckoo
lays
from
5
to
20
eggs
in
specified
range,
named
ELR
(egg
laying
radius)
[43].
ELR
is
defined
as
follow:
ELR
=∝
×
Number
of
current
cuckoo
s
eggs
Total
number
of
eggs
×
(var
hi
−
var
low
)
(4)
∝
is
an
integer,
supposed
to
handle
the
maximum
value
of
ELR,
the
var
hi
and
var
low
are
variable
limits.
The
eggs
which
have
more
similarities
to
the
host
birds’
eggs
have
a
chance
to
survive
and
grow
up.
The
density
of
the
survived
eggs
in
an
area
shows
the
suitability
of
that
area.
Cuckoo
tends
to
area
which
is
more
suitable
for
laying
eggs.
Therefore
the
position
with
more
survived
eggs
is
the
term
that
COA
optimizes.
The
remained
eggs
hatch
and
turn
into
a
mature
cuckoo.
The
habitat
of
each
newly
grown
cuckoo
is
evaluated.
In
the
new
population,
the
cuckoos
who
live
in
worst
habitats
should
be
killed
to
limit
the
population
to
the
possible
maximum
number
of
cuckoos
can
live
in
environment.
In
the
next
step
the
cuckoos
are
clustered
and
the
best
group
is
selected
as
goal
habitat.
Then
the
new
cuckoo
population
immigrates
towards
this
best
habitat.
Each
cuckoo
flies
of
all
the
way
to
the
best
habitat
and
also
has
deviation
of
ϕ
radians.
Then,
this
new
cuckoo
population
starts
to
lay
eggs
in
some
random
nests
inside
its
egg
laying
radius.
This
procedure
is
repeated
until
the
stopping
criteria
are
satisfied
[43].
3.
Asexual
Reproduction
Optimization
(ARO)
Asexual
Reproduction
Optimization
(ARO)
is
an
effective
opti-
mization
technique
inspired
from
budding
method
of
asexual
reproduction,
known
as
a
remarkable
biological
phenomenon,
which
proposed
in
[44,45].
In
this
method,
each
individual
is
indi-
cated
by
a
binary
string,
similar
to
the
binary
representation
in
evolutionary
techniques.
A
decision
variable
vector,
X
=
(x
1
,
x
2
,
剩余13页未读，继续阅读
2022-07-14 上传
2022-07-14 上传
2021-02-11 上传
2021-10-03 上传
2021-02-08 上传
2015-04-02 上传
2021-11-22 上传
2021-04-28 上传
ali_69426
- 粉丝: 0
- 资源: 2
我的内容管理
展开
最新资源
- 探索AVL树算法：以Faculdade Senac Porto Alegre实践为例
- 小学语文教学新工具：创新黑板设计解析
- Minecraft服务器管理新插件ServerForms发布
- MATLAB基因网络模型代码实现及开源分享
- 全方位技术项目源码合集：***报名系统
- Phalcon框架实战案例分析
- MATLAB与Python结合实现短期电力负荷预测的DAT300项目解析
- 市场营销教学专用查询装置设计方案
- 随身WiFi高通210 MS8909设备的Root引导文件破解攻略
- 实现服务器端级联：modella与leveldb适配器的应用
- Oracle Linux安装必备依赖包清单与步骤
- Shyer项目：寻找喜欢的聊天伙伴
- MEAN堆栈入门项目： postings-app
- 在线WPS办公功能全接触及应用示例
- 新型带储订盒订书机设计文档
- VB多媒体教学演示系统源代码及技术项目资源大全
