Path Recognition for Outdoor Navigation Using Artifical Neural

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PATH RECOGNITION FOR

OUTDOOR NAVIGATION

USING ARTIFICAL NEURALNETWORKS

TRESA MATHEW



INTRODUCTION

Most desirable feature of mobile robot -

Autonomous navigation capability.

Indoor navigation can be made possible by

obstacle avoidance.

Identify Navigable and non-navigable regions.

After detection, algorithms on path planning and

reactive obstacle avoidance is performed.



REAL ENVIRONMENTS



METHODOLOGY

A simple vision based terrain classification method.

In block-based classification method - image pixels

are grouped to generate a unique feature that

represent the group.



RGB Color spaces

Each color can be defined by the quantities of R

(red), G (green) and B (blue) components.

The feature of pixel- block

weighted average of pixel occurrence in pixel-block.

RGB entropy- calculating freq. of pixel in pixel-block.



HSV Color Spaces

Each pixel can be defined by the quantity variation

of hue, saturation and value (brightness).

The features of pixel-block

Hue weighted average

Hue entropy Saturation weighted average

Saturation entropy



It is the artificial representation of human brain that

tries to simulate its learning process.

It consists of a pool of simple processing units which

communicate by sending signals to each other over a

large number of weighted connections.

Artificial Neural Network



Operation is based on parallel processing .

Properties ² adaptability, ability to learn, ability of

generalization, organize data

Network topology:-

Feed forward network- data flow from input to output

units is strictly feed-forward. i.e. allow neuron

connections between two different layers. Recurrent network- contain feedback connections. i.e.;

connections between neurons of the same layer.



Multi-layer Perceptron

A feed forward neural network model.

Contains one or more hidden neuron layers between

input and output layers.

Output layer

Input layer



Learning algorithm used ² back propagation algorithm.

change the weights connected to the network hidden neuronlayer, based on the amount of error in the output compared tothe expected results.

If RGB is evaluated feature then input layer has 3 neurons, onefor each channel.

If combination of RGB and H entropy are evaluated then theinput layer has 4 neurons.

Output layer contains only 1 neuron

Returns 1- then pixel-block is navigable. Returns 0- then pixel-block is non-navigable.



Experiments and results

Scenario 1, leaves on the way Scenario 2, texture of blocks Scenario 3 ,shadows on theway



Features evaluated

RGB value

RGB Entropy

HSV hue HSV hue entropy

HSV saturation

HSV saturation entropy

Its combination



Network response

Network provide responses between 0 and 1.

If result <=0.4, then the region is classified as non-

navigable.

If result >=0.6, then the region is classified as

navigable.

If result>0.4 and result<=0.6, then the region is

classified as un-known, which is actually an errorvalue.



Experiment result

Blue-scenario 1

Red-scenario 2

Yellow-scenario 3

Result ²RGB and

RGB entropy

combination

Input featuresHit rate



Visualization of Neural network Output

Cyan-navigable region

Magenta-non-navigable

Yellow-unknown region

Scenario 1

Scenario 3

Scenario 2



Conclusion & Future work

Autonomous navigation ²main capability of robots.

Using ANN terrain can be classified as navigable ornon-navigable region.

Safe navigation is made possible for robots.

Combining laser mapping will provide more depth

information.

Evaluate more complex scenarios.



References

Patrick Y. Shinzato, Leandro C. Fernandes, Fernando S. Osorio, Denis F.Wolf, ́ Path Recognition for Outdoor Navigation Using Artificial NeuralNetworks: Case Studyµ, feb 2010,p-1437 to1442

Ben Krose and Patrick van der Smagt, µAn introduction to neural networksµ8th edition, 1996

A. Filipescu, I. Susnea, V. Minzu, G. Vasiliu and S. Filipescu, ´ObstacleAvoidance and Path Following Control of a WMR used as Personal RoboticAssistantµ, 18th Mediterranean Conference on Control & Automation, june2010,p- 1555 to 1560.

Dr. Vijayan K. Asari, Old Dominion University Vision Lab´Robotic TechnologyDevelopment in Vision Labµ, October 23, 2008

Howard Demuth, Mark Beale, ́ Neural Network Toolbox, for use withmatlabµ, User·s Guide Version 4.

Leonardo Noriega, ́ Multilayer Perceptron Tutorialµ, November 17, 2005.



THANK YOU

Path Recognition for Outdoor Navigation Using Artifical Neural

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