Four-Dimensional Path Planning Methodology for Collaborative Robots Application in Industry 5.0
Robotics,
Год журнала:
2025,
Номер
14(4), С. 48 - 48
Опубликована: Апрель 11, 2025
Industry
5.0
is
a
developing
phase
in
the
evolution
of
industrialization
that
aims
to
reshape
production
process
by
enhancing
human
creativity
through
utilization
automation
technologies
and
machine
intelligence.
Its
central
pillar
collaboration
between
robots
humans.
Path
planning
major
challenge
robotics.
An
offline
4D
path
algorithm
proposed
find
optimal
an
environment
with
static
dynamic
obstacles.
The
time
variable
was
embodied
enhanced
artificial
fish
swarm
(AFSA).
methodology
considers
changes
robot
speeds
as
well
times
at
which
they
occur.
This
order
realistically
simulate
conditions
prevail
during
cooperation
humans
environment.
A
method
for
calculating
time,
including
speed
formation,
presented.
safety
value
obstacles,
coefficients
importance
terms
agent’s
distance
ending
point,
obstacles
were
introduced
objective
function.
obstacle
variation
are
also
proposed.
applied
simulated
real-world
challenges
using
industrial
robotic
arm.
Язык: Английский
Hybrid path planning algorithm for robots based on modified golden jackal optimization method and dynamic window method
Expert Systems with Applications,
Год журнала:
2025,
Номер
unknown, С. 127808 - 127808
Опубликована: Апрель 1, 2025
Язык: Английский
A multi-strategy enhanced dung beetle algorithm for solving real-world engineering problems
Artificial Intelligence Review,
Год журнала:
2025,
Номер
58(8)
Опубликована: Май 23, 2025
Язык: Английский
An innovative hybrid method combining grey wolf and marine predator optimization techniques for global and constrained problem-solving
The Journal of Supercomputing,
Год журнала:
2025,
Номер
81(8)
Опубликована: Май 29, 2025
Язык: Английский
Enhanced Nutcracker Optimization Algorithm with Hyperbolic Sine–Cosine Improvement for UAV Path Planning
Biomimetics,
Год журнала:
2024,
Номер
9(12), С. 757 - 757
Опубликована: Дек. 12, 2024
Three-dimensional
(3D)
path
planning
is
a
crucial
technology
for
ensuring
the
efficient
and
safe
flight
of
UAVs
in
complex
environments.
Traditional
algorithms
often
find
it
challenging
to
navigate
obstacle
environments,
making
quickly
identify
optimal
path.
To
address
these
challenges,
this
paper
introduces
Nutcracker
Optimizer
integrated
with
Hyperbolic
Sine–Cosine
(ISCHNOA).
First,
exploitation
process
sinh
cosh
optimizer
incorporated
into
foraging
strategy
enhance
efficiency
nutcracker
locating
high-quality
food
sources
within
search
area.
Secondly,
nonlinear
function
designed
improve
algorithm’s
convergence
speed.
Finally,
that
incorporates
historical
positions
dynamic
factors
introduced
influence
position
on
process,
thereby
improving
accuracy
retrieving
stored
food.
In
paper,
performance
ISCHNOA
algorithm
tested
using
14
classical
benchmark
test
functions
as
well
CEC2014
CEC2020
suites
applied
UAV
models.
The
experimental
results
demonstrate
outperforms
other
across
three
suites,
total
cost
planned
paths
being
lower.
Язык: Английский
Unmanned combat aerial vehicle path planning in complex environment using multi-strategy sparrow search algorithm with double-layer coding
Journal of King Saud University - Computer and Information Sciences,
Год журнала:
2024,
Номер
unknown, С. 102255 - 102255
Опубликована: Ноя. 1, 2024
Язык: Английский