Mathematics,
Journal Year:
2024,
Volume and Issue:
12(24), P. 4017 - 4017
Published: Dec. 21, 2024
Aiming
at
some
shortcomings
of
the
genetic
algorithm
to
solve
path
planning
in
a
global
static
environment,
such
as
low
efficiency
population
initialization,
slow
convergence
speed,
and
easy-to-fall-into
local
optimum,
an
improved
is
proposed
problem.
Firstly,
environment
model
established
by
using
grid
method;
secondly,
order
overcome
difficulty
initialization
method
with
directional
guidance
proposed;
finally,
balance
optimization
searching
speed
up
solution
non-common
point
crossover
operator,
range
mutation
simplification
operator
are
used
combination
one-point
traditional
obtain
algorithm.
In
simulation
experiment,
Experiment
1
verifies
effectiveness
this
paper.
The
success
rates
Map
1,
2,
3,
4
were
56.3854%,
55.851%,
34.1%,
24.1514%,
respectively,
which
higher
than
two
methods
compared.
2
(IGA)
paper
for
planning.
four
maps,
compared
five
algorithms
shortest
distance
achieved
all
them.
experiments
show
that
has
advantages
Mathematics,
Journal Year:
2025,
Volume and Issue:
13(5), P. 717 - 717
Published: Feb. 23, 2025
Mobile
robots
play
a
pivotal
role
in
advancing
smart
manufacturing
technologies.
However,
existing
Obstacle
avoidance
path
Planning
(OP)
algorithms
for
mobile
suffer
from
low
stability
and
applicability.
Therefore,
this
paper
proposes
an
enhanced
Secret
Bird
Optimization
Algorithm
(SBOA)-based
OP
algorithm
to
address
these
challenges,
termed
AGMSBOA.
Firstly,
adaptive
learning
strategy
is
introduced,
where
individuals
enhance
the
diversity
of
algorithm’s
population
by
summarizing
relationships
among
candidates
varying
quality,
thereby
strengthening
ability
locate
globally
optimal
obstacle
regions.
Secondly,
group
incorporated
dividing
into
teaching
groups,
enhancing
exploitation
capabilities,
improving
accuracy
planning,
reducing
actual
runtime.
Lastly,
multiple
evolution
proposed,
which
balances
exploration/exploitation
phases
analyzing
nature
different
individuals,
escape
suboptimal
traps.
Subsequently,
AGMSBOA
was
used
solve
problem
on
five
maps
two
problems
real-world
environments.
The
experiments
illustrate
that
achieves
more
than
5%
performance
improvement
length
100–win
rate
runtime
metrics,
as
well
faster
convergence
solution.
proposed
efficient,
robust,
robust
method
robots.
Biomimetics,
Journal Year:
2025,
Volume and Issue:
10(3), P. 142 - 142
Published: Feb. 26, 2025
Automated
Guided
Vehicles
(AGVs)
face
dynamic
and
static
obstacles
in
the
process
of
transporting
patients
medical
environments,
they
need
to
avoid
these
real
time.
This
paper
proposes
a
bionic
obstacle
avoidance
strategy
based
on
adaptive
behavior
antelopes,
aiming
address
this
problem.
Firstly,
traditional
artificial
potential
field
window
algorithm
are
improved
by
using
characteristics
antelope
migration.
Secondly,
success
rate
prediction
range
AGV
navigation
adding
new
force
points
increasing
size.
Simulation
experiments
were
carried
out
numerical
simulation
platform,
verification
results
showed
that
proposed
can
at
same
In
example,
path
planning
is
increased
34%,
running
time
reduced
33%,
average
length
1%.
The
method
help
realize
integration
“dynamic
static”
effectively
save
AGVs
transport
patients.
It
provides
theoretical
basis
for
realizing
rapidly
loading
environments.
Robotica,
Journal Year:
2025,
Volume and Issue:
unknown, P. 1 - 24
Published: March 10, 2025
Abstract
Global
path
planning
using
roadmap
(RM)
path-planning
methods
including
Voronoi
diagram
(VD),
rapidly
exploring
random
trees
(RRT),
and
probabilistic
(PRM)
has
gained
popularity
over
the
years
in
robotics.
These
global
are
usually
combined
with
other
techniques
to
achieve
collision-free
robot
control
a
specified
destination.
However,
it
is
unclear
which
of
these
best
choice
compute
efficient
terms
length,
computation
time,
safety,
consistency
computation.
This
article
reviewed
adopted
comparative
research
methodology
perform
analysis
determine
efficiency
optimality,
consistency,
time.
A
hundred
maps
different
complexities
obstacle
occupancy
rates
ranging
from
50.95%
78.42%
were
used
evaluate
performance
RM
methods.
Each
method
demonstrated
unique
strengths
limitations.
The
study
provides
critical
insights
into
their
relative
performance,
highlighting
application-specific
recommendations
for
selecting
most
suitable
method.
findings
contribute
advancing
by
offering
detailed
evaluation
widely
AIP Advances,
Journal Year:
2024,
Volume and Issue:
14(8)
Published: Aug. 1, 2024
In
this
paper,
a
novel
Gyro
Fireworks
Algorithm
(GFA)
is
proposed
by
simulating
the
behaviors
of
gyro
fireworks
during
display
process,
which
adopts
framework
multi-stage
and
multiple
search
strategies.
At
beginning
iteration,
are
full
gunpowder;
they
move
via
Lévy
flight
spiral
rotation,
sprayed
sparks
widely
distributed
more
balanced,
an
effective
global
exploration
method.
later
iteration
stages,
due
to
consumption
gunpowder,
gradually
undergo
aggregation
contraction
conducive
group
exploit
local
area
near
optimal
position.
The
GFA
divides
iterative
process
into
four
phases,
each
phase
different
strategy,
in
order
enhance
diversity
population
balance
capability
space
exploitation
space.
verify
performance
GFA,
it
compared
with
latest
algorithms,
such
as
dandelion
optimizer,
Harris
Hawks
Optimization
(HHO)
algorithm,
gray
wolf
slime
mold
whale
optimization
artificial
rabbits
optimization,
33
test
functions.
experimental
results
show
that
obtains
solution
for
all
algorithms
on
76%
functions,
while
second-placed
HHO
algorithm
only
21%
Meanwhile,
has
average
ranking
1.8
CEC2014
benchmark
set
1.4
CEC2019
set.
It
verifies
paper
better
convergence
robustness
than
competing
algorithms.
Moreover,
experiments
challenging
engineering
problems
confirm
superior
over
alternative
Research Square (Research Square),
Journal Year:
2024,
Volume and Issue:
unknown
Published: April 23, 2024
Abstract
Aiming
at
the
problem
that
Automated
guided
vehicle
(AGV)
faces
dynamic
and
static
obstacles
in
process
of
transporting
patients
medical
environment
needs
to
avoid
real
time,
inspired
by
behavior
antelopes
during
migration,
this
paper
proposes
a
bionic
obstacle
avoidance
strategy
based
on
adaptive
antelopes.
The
traditional
artificial
potential
field
window
algorithm
are
improved
using
characteristics
antelope
migration.By
adding
new
force
points
improving
size,
success
rate
prediction
range
AGV
navigation
improved.Simulation
experiments
were
carried
out
through
numerical
simulation
platform,
verification
results
showed
that:The
proposed
can
same
time.
In
example,
path
planning
is
increased
34%,
running
time
reduced
33%,
average
length
1%.
method
realize
integration
“dynamic
static”
patients,
effectively
save
AGV.It
provides
theoretical
basis
for
realizing
rapid
loading
environment.
