AESWARM

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Mert DoğarHazar İlhanDünya Değirmenci

Software Framework

Four pieces of software: GUI/ controller Simulator SOM (multiple instance) SOM Launcher (assistant to simulator)

Connect to each other through TCP/IP

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Software Framework

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Environment vs Robot

Environment in reality is demanding; Non-deterministic Dynamic Continous

We utilize 6-way proximity data Ambient light intensity Infared communication to handle robot’s

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The Robots

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• Two DC motors on the sides, one free wheel at the rear

• Chassis and wheels made of white polyethylene

• Run on Nokia batteries, rechargeable

• Wheels partially clad with o-rings for sufficient grip

The Robots

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Power ConsumptionMain consumers of power on the robot are

• dsPic33: 300mA absolute maximum (average around 100mA)

• DC motors: 250mA measured maximum (normally around 80-100mA)

• IR emitters: 100mA absolute maximum

The Robots

Current Abilites:

Able to perform proximity analysis from infrared density Working with outputs derived

from SOMs Can select source SOM

according to ambient

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The Environments

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• Three constraints while designing the environments:

– Corridor width, must not cause congestion

– Lines of sight, must create different conditions of communication

– Charger placement, must allow various scenarios

• As such, we created three different environments for the robots to live in.

– Each fit in an area of 110x97cm

– Wall height: 13cm

Future work

2 microphones to capture auditory data 2D localization techniques on a microphone

array A CMOS camera to capture visual data

Image processing and pattern recognition techniques

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IR emitter

IR emitter

IR sensors

IR emitterIR emitter

IR sensor

Ambient light sensor

Voltage divider for battery level

Microphone inputs

CMOS Camera

Extra Memory

All robots contain a 2gb non-volatile memory Used for logging Contains all soms(i.e. genes) available to a

robot Contains starting training vectors

Training vectors will be overwritten when passing a generation

File format is FAT16

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Self-Charging

We want our swarm units to automatically detect low power and start search for charging area.

This will be carried out by following the nearby walls Listening to communication buffer if anyone who found the charging area is yelling out the

location.

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Charging area

Adaptive resonance theory (ART2)

Reduced boltzmann machine used with contrastive divergence

Back propagation algorithm

Self-Organizing Maps by Kohonen

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Self-Organizing Maps

Proposed by Kohonen Commonly used for data

classification Unsupervised learning, noise tolerant

Maps an arbitrary N dimentions of data to commonly 1 or 2 dimensions Data clustering Output selection

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Best-Matching-Unit

Behavioral State Machine

Functional Diagram

References

Kohonen, T. (1997). Self-Organizing Maps, Second Edition. Heidelberg, Germany: Springer- Verlag.

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