Communication and Distributed Control in Multi-

Agent SystemsPreliminary Model of Micro-unmanned Aerial Vehicle (MAV) Swarms

Fabio RuiniAdaptive Behaviour and Cognition Research Group (ABC)School of Computing, Communications and Electronics

University of Plymouth, UK

VENICE (ITALY), 10-11 JANUARY 20084TH EUCOGNITION SIX-MONTHLY MEETING

Acknowledgments: euCognition Network Action NA097-3EOARD European Office of Aerospace Research and Development (Grant 073075)

Introduction

This work focuses on the use of Multi-Agent Systems for the modelling of Micro-unmanned Aerial Vehicles (MAVs) in a distributed control task.

The task regards a search scenario in the context of security and urban counter-terrorism.

The main goals of this research are:

1. to demonstrate how a MAVs swarm can successfully be made autonomous, using evolutionary and neural computation techniques;

2. to study how the communication between the team-mates can impact the performance of these flying robots.

Simulated environment

610 m (710 pixels)

65

0 m

(7

60

pix

els

)

Obstacle Width Height Starting X Starting Y Final X Final Y

0 130 82 180 44 310 126

1 130 82 180 150 310 232

2 95 190 408 38 503 228

3 55 230 643 17 698 247

4 110 90 280 213 390 303

5 90 95 231 263 321 358

6 83 93 417 266 500 359

7 133 213 577 247 710 460

8 50 95 109 403 159 498

9 90 140 231 400 321 540

10 90 142 419 392 509 534

11 45 88 74 498 119 586

12 49 38 274 564 323 602

13 100 55 244 602 344 657

14 35 18 430 534 465 552

15 90 123 420 552 510 675

16 33 59 387 620 420 679

17 53 171 592 482 645 653

18 44 65 213 698 257 763

Obstacles distribution:

(Picture found on: http://www.aquiva.co.uk/canarywharf)

AeroVironment WASP Block III

Length 38 cm (15 in)

Wingspan 72 cm (28.5 in)

Weight 430 g (0.95 lb)

Speed46-65 km/h (25-40

mph)

Endurance 45 min

Propulsion electric motor*Source: AeroVironment web site

http://www.avinc.com/downloads/WASP-III_datasheet_6_5_07.pdf

Technical specifications*:

Neural Network controller

TARGETDISTANCE

TARGETANGLE

ULTRA-SONICPERCEPTION

STEERING DETONATION

Distance from the targetSince the simulated environment can be seen as a Cartesian plane, the distance between the current MAV and the target is calculated as the Euclidean distance between their centre points and then discretized.Angle from the targetThe angle between the current MAV and the target is measured basing on its facing direction and then discretized using a Gray code-based coding.Ultra-sonic perceptionEach MAV is endowed with three ultra-sonic sensors, respectively oriented at 0°, 45° and 315° (-45°), able to detect the presence of an obstacle until 30 pixels far.

SteeringThe output neuron dedicated to the steering is a continuous neuron whose output value ranges from -1 (340°/-20°) to +1 (+20°).

DetonationThe output neuron dedicated to the detonation is a Boolean neuron.

0 : do nothing;1: detonate.

Simulator: a quick overview

Neural network computational capability: neurons distribution along the various layers

Genetic algorithm: number of seeds, number of generations (for a single seed), number of swarms belonging to the population, number of tests to be carried out on every swarm

Genetic operators: mutation probability, mutation amount

Various controls: enable/disable graphics view, background and statistics visualizations, manual control of the visualization speed, pause, zoom level, visualization speedControl buttons: start/stop evolution, start/stop test, exit

Some swarms in action

End of the evolution

Let’s see how the MAVs perform:

Beginning of the evolution

Evolution resume

Conclusions and future works

In this preliminary model we have demonstrated how a neural network controller for MAVs swarms can be successfully evolved through multi-agent systems.

Plans for future work:

Use movable target, “robust” target;

Study different communication capability/protocols (including self-emergent lexicons);

Develop 3D simulator (physics library).

Contacts, links and publications

• Contacts:

• e-mail: fabio.ruini@plymouth.ac.uk

• web: http://www.fabioruini.eu

• Skype: fabio.ruini

Links:

Project home page: http://www.tech.plym.ac.uk/soc/research/ABC/plymav/

Adaptive Behaviour and Cognition Research Group:http://www.tech.plym.ac.uk/soc/research/ABC/

Publications:

Whitepaper: http://www.eucognition.org/network_actions/NA097-3_outcome.pdf

End...