Evolving a Team of Self-organizing UAVs to Address Spatial Coverage Problems

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Evolving a Team of Self-organizing UAVs to Address Spatial Coverage Problems István Fehérvári, Wilfried Elmenreich and Evsen Yanmaz Mobile Systems Group/Lakeside Labs Institute for Networked and Embedded Systems Alpen-Adria Universität Klagenfurt

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Talk given at EMCSR 2012 conference in Vienna.

Transcript of Evolving a Team of Self-organizing UAVs to Address Spatial Coverage Problems

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Evolving a Team of Self-organizing UAVs to Address Spatial Coverage ProblemsIstván Fehérvári, Wilfried Elmenreich and Evsen YanmazMobile Systems Group/Lakeside LabsInstitute for Networked and Embedded SystemsAlpen-Adria Universität Klagenfurt

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Overview

• Unmanned areal vehicles

• The coverage problem

• Discrete simulation model

• Evolutionary approach

• Results

• Conlusion and outlook

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UAVs

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Battery-powered UAVs

• Quadcopter platform with onboard sensors and electronic for flight stabilization

• Attached cameras for sensing the environment

• GPS receiver for autonomous waypoint flights

• Limitations on payloads, flight time, weather conditions

www.microdrones.de               www.asctec.de

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Small UAV Network for Emergency Assistance

Mission Control

UAV Network

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Coverage/Detection Problem

• Wireless sensor networks– Civil

• Environmental monitoring: wildfires, volcanoes, glaciers, storms, agriculture fields

• Communication assistance: serve as data fusion centers; serve as mobile base stations (relay)

• Video surveillance: traffic

monitoring, convoy protection

– Military, security• Battlefield assistance; target

detection and tracking; search and destroy; border monitoring

• Coverage problem in robotics– Snow removal, lawn

mowing, car-body painting, floor cleaning, etc.

• Cellular decomposition

• Complete versus randomized (probabilistic)

• Known layout of the environment versus sensor-based coverage

• Time-to-complete: shortest path, minimum energy, minimum number of turns, etc

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Planning and Monitoring Flight Paths

• Example for flight paths of two UAVs (green/red color)

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Simplified Discrete Simulation Model

• finite two-dimensional lattice

• each cell can contain at most one agent or obstacle

• agent can move to one of four directly neighboring cells (vN neighborhood)

• Goal: have each cell being visited at least once after minimum time

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Keep it Simple!

• No a priori knowledge about map size/position of obstacles

• No explicit communication among UAVs• No position estimation mechanism

• No map building

• Looking for a self-organizing online algorithm

• Until now, planned offline algorithms have been used (e.g. using a TSP solver)

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Wanted: the right UAV behavior model

• Controls the UAV as autonomous agent• Processes inputs (from sensors) and produces

output (to actuators)

Control System„Agent‘s Brain“

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EvolutionaryApproach

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Evolving the Control System

• Simulation of target system as testing playground

• Define goal via fitness function (e.g., maximize throughput in a network)

• Run evolutionary algorithm to derive behavior fulfilling the given goal

• Representation must be evolvable• Mutation

• Recombination

• difficult with an algorithm represented in C or Java code…

System modelGoals (fitness function)Simulation of problem

Explore solutions

Evaluate & Iterate

Analyze results

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Artificial Neural Networks

• Each neuron sums up theweighted outputs of the other connected neurons

• The output of the neuronis the result of an activation function (e.g. step, sigmoid function) applied to this sum

• Neural networks are distinguished by their connection structure– Feed forward connections (layered)

– Recursive (Ouput neurons feed back to input layer)

– Fully meshed

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Neural Networks are Evolvable

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A Framework for Evolutionary Design

• FREVO (Framework for Evolutionary Design)

• Modular Java tool allowing fast simulation and evolution

• „Frevo“ means also a hot, „boiling“ dance around here

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Framework for Evolutionary Design

• FREVO defines flexible components for – Controller representation

– Problem specification

– Optimizer

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Modeling the Coverage Problem in FREVO

• Basically, we need a simulation of the problem• Interface for input/output connections to the

agents– Inputs for detecting obstacles

– Inputs for detecting other drones– Navigation output

• Feedback from a simulation run -> fitness value– spatial coverage (number of cells visited at least once

divided by the total number of unobstructed cells)

– completion time (number of simulation steps needed

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ResultsPhoto: wikipedia.org

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Algorithms under Test

• Non-cooperative evolved algorithms– UAVs are not aware of other UAVs– Basically evolving a „better“ random walk

• Cooperative evolved algorithm– Extra input to recognize meeting other UAVs

• Belief-based algorithm– Handcrafted solution based on random direction– Avoids cluttering of UAVs at border/meeting situations

• Reference Algorithms– Random walk– Random direction

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Coverage Snapshot for 10 UAVs

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Performance Comparison

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Conclusion and Outlook

Photo: wikipedia.org

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Conclusion and Outlook

• Three promising algorithms• Cooperation feature upon meeting of two UAVs

does not significantly improve results– for realistic density of drones

• Implementation in real system feasible– small computational effort

• Future work– Compare with a priori planning algorithms

– Challenges: dynamic environment, continuous space model

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Visit us!

• (open source):

www.frevotool.tk• Project MESON (Design Methods for Self-

Organizing Systems):meson.lakeside-labs.com

• Project cDrones:www.cdrones.com

• Lakeside Labs Clusterwww.lakeside-labs.com

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Thank you very much for your attention!

A short summary of the talk and the slides will be available at http://demesos.blogspot.com