2. About Project:
• This Project deals with doing some
operations in Agriculture
autonomously. By implementation of
this method a lot of time and energy
is saved.
• By using this system a Farmer can
save his time and money for
Cultivating crops on a mass scale.
3. Automated Guided Vehicles
• The navigation of these vehicles is managed
using GPS.
• Currently the technology is used to navigate
passenger cars.
• We are using this technology in order to
achieve navigation on field.
6. Major components used:
• Commercial Tractor
• PLC/PID controller
• Encoders
• On board computer
• Ultrasonic Range finder,etc.
7. Architecture of System
• The parameters of the motion are driving
speed and steering angle which determine the
evolution of the position and orientation of the
AGV
• The inputs are the encoder signal from left and
right rear wheels.
• The digital output is converted to analog
signal to drive amplifier of the driving motor
and steering motor on front wheel.
8.
9.
10. Kinematics of AGV
• The required path of the AGV is be defined by line and circle as shown in
figure 3. The path is constructed in order to guide the vehicle movement
and stored in the memory of the PLC. During the vehicle movement an
error will occur between the actual position P(t) of the AGV and the
defined path as shown figure
11. Kalman Filter
• “The Kalman Filter is an estimator for what is called
the linear-quadratic problem, which is the problem
of estimating the instantaneous ‘state’ of a linear
dynamic system perturbed by white noise – by using
measurements linearly related to the state but
corrupted by white noise.
12. Kalman Filter Uses
• Estimation
– Estimating the State of Dynamic Systems
– Almost all systems have some dynamic
component
• Performance Analysis
– Determine how to best use a given set of sensors
for modeling a system
13. • Vector Kalman filter is formulated with state equations for linear
system as
• following;
• Where x(k ) k and x(k-1) are state transition matrix by column
vector at time k and k −1, respectively. w( k-1) − is a noise process
which is white obtained by-zero mean and independent of all
others in dimension of column vector. A is a system transition
coefficients with dimension of square matrix. y(k) is the
measurement state output matrix at time k . C is the measurement
or observation matrix. v(k) represents an additive noise matrix
during measurement process at time k .
16. PLC
• PLCs have been gaining popularity on the factory floor and
will probably remain predominant
• for some time to come. Most of this is because of the
advantages they offer.
• • Cost effective for controlling complex systems.
• • Flexible and can be reapplied to control other systems
quickly and easily.
• • Computational abilities allow more sophisticated control.
• • Trouble shooting aids make programming easier and
reduce downtime.
• • Reliable components make these likely to operate for
years before failure.
