Outline of Course Project for Fuzzy Logic Systems-EE5326

8/6/2019 Outline of Course Project for Fuzzy Logic Systems-EE5326

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Outline of Course Project for Fuzzy

Logic Systems-EE5326

Obstacle Avoidance in the Navigation of HolonomicVehicles/Robots using Fuzzy Rule Based Inference

SystemsProfessor-Dr. Tracy Liang

Student-Kunal Vora

Id-1000684121


2/22

Inspirations

Robot navigation in the Controlled

environment like in Manufacturing and

packaging uses Neuro-fuzzy systems. This

project is only a small part of its realization.

Failure of simulation of Robot path planning

using potential fields made in a course last

semester, under certain complex scenarios.


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Classification of Approaches Used

Modeling based Approach

Sensor Based (Behavior Architecture)

Approach Hybrid approach


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Model Based Approach

Modeling the environment to generated the path.Ex-Road Mapping, Cell Decomposition, PotentialFields.

Some of these techniques can be used to find theshortest path from starting to goal point.

Drawback-It depends heavily on the environmentmodel that is developed to generate a safe path

.Since it is difficult to find accurate models thismethod is used mostly in controlled environmentlike industries, manufacturing and packaging.


5/22

Sensor Based Approach

Feedback from sensor. Control command generatedbased on Sensor data. Ex-Goal attraction, wallfollowing, Obstacle avoidance.(Reactive system).

Each type of sensor feedback generates a typicalbehavior in the robot/vehicle that it followsunder each specific conditions.

Due to reactive bahavior of this systems, they cannavigate efficiently in dynamic environment.

Drawback-Robot/vehicle may not reach the desiredtarget , even if a safe path exists between startingand ending points


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Hybrid Approach

A compromise between the optimality of

model based approach and adaptability of

Sensor based approach.

It starts with the model based approach to

generate a optimal path, and then uses a

sensor feedback based controller, that helps it

to navigate ,avoiding the obstacles, which are

unknown in the model created.


7/22

About the present project

It assumes that the vehicle/robot is Holonomic. Itcan move in all directions without anyconstraints.

All obstacles are static. Goal is static. Vehicle/robot gets feedback from sensors that

measures distance of its present location to allobstacles and goal and their angular positions.

It uses Fuzzy Rule based system to makedecisions about the directions to be moved in,once it realizes that the obstacle is in its vicinity.


8/22

About the present project-Conti

0 2 4 6 8 10 12 0

2

4

6

8

10

12

Target

O B S 1

O B S 2 O B S 3

O B S 4

R O B O T Angle measurement


9/22

Some Description-

About the execution

The robot/vehicle starts from its origin position with aconstant speed, towards the Target .

At each step it calculates the Euclidian distancebetween itself and each of the obstacles and between

itself and the Target point. It also measures the angle (w.r.t X-axis) between the

line from present position to goal and the lines fromthe present position to each of the obstacles.

This data can be considered as the feedback from thesensors either proximity sensors or the positionsensors.


10/22

Some Description about the

Execution(Conti)

When the distance between closest obstacle and itselfcrosses a certain threshold, it activates an inferencesystem which is based on Fuzzy Rules about- what thebehavior of vehicle/robot should be? when it

encounters such a condition. The FIS uses the data of the Angular measurements

taken during the motion and the present position.

The output of the FIS is used to decide -Robot/Vehicleis supposed to in which direction? to avoid collisionand reach the target point safely.

This FIS is activated every time the Vehicle/robotencounters an obstacle.


11/22

Input Membership Functions of FIS

(The values are never fixed)

Angle between present position to target line and X-

axis

Obs in wayObs just

on leftObs just

on right

Obs on leftObs on right

Obs on

side Obs on

side


12/22

Outline of Inference and Rules

Complete If-Then Rules are used.

Since the robot encounters one obstacle onlyat a time , we can consider that mostly thissystem will be singleton type. But attemptswill be made to make it more comprehensivenon-singleton system.

The input parameter to the Input functions isthe angle towards the obstacle relative to theangle towards the Target point.


13/22

Rules Format

If the obstacle is in way, disallow front movement. Disallow front movement, means that go either left

or right. This is realized in coding by moving on eitherof any axis and not along both the axes.

Ifthe obstacle is on just left , then move rightfast. Move right fast is realized in coding by moving morenumber of steps along the right axis


14/22

Inference And Defuzzification

If more than one input MF are fired then Max-out of two will be used.

In deriving the inference, if more than one

rules are encountered(fired) then the type ofComposition used will depend on , which onegives the better output and decision.

For getting the correct output theconventional Defuzzification methods havefailed .[1].


15/22

De-fuzzification

Height De-fuzzification will be Implemented

It has been proven better in this kind ofapplication which needs more accuracy then

smoothness.

Ex-If Output Command is of the form given onnext page , and if we use, and if we used

centroid de-fuzzification, then the robot willmove in wrong direction leading to failure ofAlgorithm.


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Defuzzification

Centroid

Defuzz

Height or

Centre of

Largest area


17/22

After Inference

After the FIS gives its inference the Robotexecutes it and goes in the direction which isaway from the obstacle Thus realizing

Obstacle avoidance behavior (attributed bythe sensor feedback).

Once the Robot/Vehicle is away from theobstacle by certain threshold distance ,itswitches back to the Goal attraction behaviorleaving the FIS alone.


18/22

Advanced Technique-Command Fusion

This technique of Command fusion has now beenused by a number of researcher in thepublications.

Same will be realized, but using De-fuzzificationprocess.

As mentioned earlier-

If the obstacle is in way ,disallow front movement.This rule will generate a Fuzzy conclusion based on FIS about the

Disallowed Direction.

Disallowed Direction is Front , so not disallowed direction is either leftor right


19/22

Conti

At the same time the goal attraction behavior

will generate an another conclusion we call it

the desired direction.

We shall fuse these two Conclusion and

defuzzify using a Conjunction operator.

Turning Direction(x)= (Desired) AND (Not Disallowed)(x)

=( Desired (x) ) (1-Disallowed(x))


20/22


21/22

Suggestions and Comments.Thank You.


22/22

References-

Uncertainty Rule based Fuzzy logic Systems-J

M Mendel.

Fuzzy logic based extension to Payton andRosenblatts Command fusion Method for

Mobile Robot Navigation -J Yen and N

Pflugger.

A number of other IEEE Publications.

Documents

Outline of Course Project for Fuzzy Logic Systems-EE5326