Automatic agriculture assistance

AUTOMATIC AGRICULTURE ASSISTANCE

By Team Zehn

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.

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.

Top View

Front View

Side View

Isometric View

Major components used:

• Commercial Tractor• PLC/PID controller• Encoders• On board computer• Ultrasonic Range finder,etc.

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.

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

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.

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

• 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 .

Automobile Voltimeter Example

Diagram for Working

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.

Thank You