SGEO: Equalization Optimizer with Hybrid Learning Strategies for UAV Path Planning in Complex 3D Environments
Abstract
Unmanned
Aerial
Vehicle
(UAV)
route
planning
is
an
intricate
issue
that
requires
the
comprehensive
consideration
of
multiple
factors
and
combination
suitable
strategies
to
achieve
efficient
safe
flight
paths.
Its
purpose
plan
a
navigational
path
for
drone
specific
task
or
avoid
obstacles.
In
practical
applications,
UAVs
are
usually
required
accomplish
tasks
in
variety
complex
environments,
resulting
feasible
paths
will
be
reduced
becomes
difficult.
Accordingly,
we
design
simple
yet
useful
planner,
called
Self-adaptive
Golden
Equilibrium
Optimization
algorithm
(SGEO).
The
proposed
combines
new
self-adaptive
approach,
acceleration
control
factor
Golden-SA
strategy
order
balance
exploitation
exploration
capabilities.
novel
used
expand
global
search
range
enhance
its
ability
exploration;
introduced
parameters
a1
a2
improve
convergence
algorithm;
helps
candidate
particles
better
between
different
dimensions
based
on
golden
ratio
sine
function,
this
enables
explore
space
more
comprehensively.
limitations
UAV
translated
into
objective
four
algorithms
compare
with
SGEO
two
circumstances.
simulation
findings
reveal
excels
at
three-dimensional
complicated
environments.
Research Square (Research Square), Год журнала: 2024, Номер unknown
Опубликована: Март 15, 2024
Язык: Английский