Applications of Key Autonomous Navigation Technologies for Unmanned Agricultural Tractors: A Review
Опубликована: Фев. 7, 2024
Unmanned
agricultural
tractor
(UAT)
represents
the
advanced
stage
of
autonomous
navigation
and
serves
as
a
core
technology
in
production.
It
reduces
operator’s
workload,
improves
operational
accuracy
efficiency.
This
article
reviews
three
aspects
key
technologies
for
UATs:
perception,
path
planning
tracking,
motion
control.
The
advantages,
shortages
these
on
UATs
are
clarified
by
analyzing
technical
principles
current
research
status.
We
conducted
summaries
analyses
existing
unmanned
solutions
different
application
scenarios
order
to
identify
bottleneck
issues.
Based
analysis
applicability
UATs,
it
can
be
seen
that
fruitful
progresses
have
been
achieved.
review
also
summarizes
common
problems
technologies.
sharing
integrating
multi-source
data
relatively
weak.
There
is
an
urgent
need
high-precision,
high-stability
sensing
equipment.
universality
methods,
efficiency
precision
tracking
improved.
necessary
develop
high
reliability
electrical
control
modules
enhance
performance.
Overall,
sensors,
high-performance
intelligent
algorithms,
reliable
hardware
factors
promoting
development
UAT
technology.
Язык: Английский
Path Tracking Control of a Large Rear-Wheel–Steered Combine Harvester Using Feedforward PID and Look-Ahead Ackermann Algorithms
Agriculture,
Год журнала:
2025,
Номер
15(7), С. 676 - 676
Опубликована: Март 22, 2025
Autonomous
driving
solutions
for
agricultural
machinery
have
advanced
rapidly;
however,
large-wheeled
harvesters
present
unique
challenges
compared
to
traditional
vehicles.
Specifically,
the
5.4
m
cutting
width,
9.2
minimum
turning
diameter,
and
rear-wheel–steered
configuration
demand
specialized
path
tracking
steering
methods.
To
address
these
challenges,
this
study
developed
an
integrated
system
combining
feedforward
PID
Look-Ahead
Ackermann
(LAA)
algorithms
with
sensors,
actuators,
embedded
control
platform.
Field
experiments
indicated
that
maintained
average
lateral
deviation
of
approximately
5
cm
on
straight-line
paths,
slightly
larger
errors
observed
only
during
or
alignment
maneuvers.
Additionally,
a
“three-cut”
method
was
implemented,
which
enhanced
accuracy
prevented
crop
damage
at
headland
turns.
Successful
field
tests
confirmed
robustness
system,
highlighting
its
practical
potential
production-level
autonomous
harvesting.
Язык: Английский
Sensing and Perception in Robotic Weeding: Innovations and Limitations for Digital Agriculture
Sensors,
Год журнала:
2024,
Номер
24(20), С. 6743 - 6743
Опубликована: Окт. 20, 2024
The
challenges
and
drawbacks
of
manual
weeding
herbicide
usage,
such
as
inefficiency,
high
costs,
time-consuming
tasks,
environmental
pollution,
have
led
to
a
shift
in
the
agricultural
industry
toward
digital
agriculture.
utilization
advanced
robotic
technologies
process
serves
prominent
symbolic
proof
innovations
under
umbrella
Typically,
consists
three
primary
phases:
sensing,
thinking,
acting.
Among
these
stages,
sensing
has
considerable
significance,
which
resulted
development
sophisticated
technology.
present
study
specifically
examines
variety
image-based
systems,
RGB,
NIR,
spectral,
thermal
cameras.
Furthermore,
it
discusses
non-imaging
including
lasers,
seed
mapping,
LIDAR,
ToF,
ultrasonic
systems.
Regarding
benefits,
we
can
highlight
reduced
expenses
zero
water
soil
pollution.
As
for
obstacles,
point
out
significant
initial
investment,
limited
precision,
unfavorable
circumstances,
well
scarcity
professionals
subject
knowledge.
This
intends
address
advantages
associated
with
each
technologies.
Moreover,
technical
remarks
solutions
explored
this
investigation
provide
straightforward
framework
future
studies
by
both
scholars
administrators
context
weeding.
Язык: Английский
Modelling the Dynamics of a Wheeled Mobile Robot System Using a Hybridisation of Fuzzy Linear Quadratic Gaussian
Опубликована: Июнь 21, 2024
Wheeled
mobile
robot
dynamics
and
suitable
controller
design
are
challenging
but
rewarding
fields
of
study.
By
understanding
the
wheeled
robots,
it
could
be
possible
to
hybrid
control
schemes
for
robots.
Since
involve
combining
individual
methods
create
a
more
effective
overall
strategy.
This
can
done
in
variety
ways,
such
as
using
fuzzy
linear
quadratic
Gaussian
control.
We
were
therefore
inspired
dynamic
models
their
designs
by
examining
robot.
The
novelty
current
paper
is
hybridize
different
robots
order
get
better
performance.
Entire
systems
simulated
MATLAB/SIMULINK
environment.
results
obtained
settling
time
response
FLQG
87.1%
over
LQG;
this
study
compared
effectiveness
previous
controllers
external
disturbances
found
peak
amplitude
improvements
71.25%.
Therefore,
proposed
use
with
Язык: Английский
A Novel Fuzzy Logic Switched MPC for Efficient Path Tracking of Articulated Steering Vehicles
Robotics,
Год журнала:
2024,
Номер
13(9), С. 134 - 134
Опубликована: Сен. 5, 2024
This
paper
introduces
a
novel
fuzzy
logic
switched
model
predictive
control
(MPC)
algorithm
for
articulated
steering
vehicles,
addressing
significant
path
tracking
challenges
due
to
varying
road
conditions
and
vehicle
speeds.
Traditional
single-model
parameter-based
controllers
struggle
with
errors
computational
inefficiencies
under
diverse
operational
conditions.
Therefore,
kinematics-based
MPC
is
first
developed,
showing
strong
real-time
performance
but
encountering
accuracy
issues
on
low-adhesion
surfaces
at
high
Then,
4-DOF
dynamics-based
designed
enhance
stability.
The
proposed
solution
strategy,
integrating
system
that
dynamically
switches
between
algorithms
based
error,
time,
heading
angle
indicators.
Subsequently,
simulation
tests
are
conducted
using
SIMULINK
ADAMS
verify
the
of
algorithm.
results
confirm
this
fuzzy-based
can
effectively
mitigate
drawbacks
approaches,
ensuring
precise,
stable,
efficient
across
adhesion
Язык: Английский
Intelligent Surface Recognition for Autonomous Tractors Using Ensemble Learning with BNO055 IMU Sensor Data
Agriculture,
Год журнала:
2024,
Номер
14(9), С. 1557 - 1557
Опубликована: Сен. 9, 2024
This
study
aims
to
enhance
the
navigation
capabilities
of
autonomous
tractors
by
predicting
surface
type
they
are
traversing
using
data
collected
from
BNO055
Inertial
Measurement
Units
(IMU
sensors).
IMU
sensor
were
a
small
mobile
robot
driven
over
seven
different
floor
surfaces
within
university
environment,
including
tile,
carpet,
grass,
gravel,
asphalt,
concrete,
and
sand.
Several
machine
learning
models,
Logistic
Regression,
K-Neighbors,
SVC,
Decision
Tree,
Random
Forest,
Gradient
Boosting,
AdaBoost,
XGBoost,
trained
evaluated
predict
based
on
data.
The
results
indicate
that
Forest
XGBoost
achieved
highest
accuracy,
with
scores
98.5%
98.7%
in
K-Fold
Cross-Validation,
respectively,
98.8%
98.6%
an
80/20
State
split.
These
findings
demonstrate
ensemble
methods
highly
effective
for
this
classification
task.
Accurately
identifying
types
can
prevent
operational
errors
improve
overall
efficiency
systems.
Integrating
these
models
into
tractor
systems
significantly
adaptability
reliability
across
various
terrains,
ensuring
safer
more
efficient
operations.
Язык: Английский