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Autonomous Controller Design for Unmanned Aerial Vehicles using

Multi-objective Genetic Programming

Choong K. Oh and Gregory J. BarlowU.S. Naval Research Laboratory

North Carolina State University

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Overview

• Problem• Unmanned Aerial Vehicle Simulation• Multi-objective Genetic Programming• Fitness Functions• Experiments and Results• Conclusions• Future Work

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Problem

Evolve unmanned aerial vehicle (UAV) navigation controllers able to:• Fly to a target radar based only on

sensor measurements• Circle closely around the radar• Maintain a stable and efficient flight

path throughout flight

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Controller Requirements

• Autonomous flight controllers for UAV navigation

• Reactive control with no internal world model

• Able to handle multiple radar types including mobile radars and intermittently emitting radars

• Robust enough to transfer to real UAVs

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Simulation

• To test the fitness of a controller, the UAV is simulated for 4 hours of flight time in a 100 by 100 square nmi area

• The initial starting positions of the UAV and the radar are randomly set for each simulation trial

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Sensors

• UAVs can sense the angle of arrival (AoA) and amplitude of incoming radar signals

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UAV Control

EvolvedController

AutopilotUAVFlight

Sensors

Roll angle

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Transference

These controllers should be transferable to real UAVs. To encourage this:

• Only the sidelobes of the radar were modeled

• Noise is added to the modeled radar emissions

• The angle of arrival value from the sensor is only accurate within ±10°

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Multi-objective GP

• We had four desired behaviors which often conflicted, so we used NSGA-II (Deb et al., 2002) with genetic programming to evolve controllers

• Each fitness evaluation ran 30 trials• Each evolutionary run had a population

size of 500 and ran for 600 generations• Computations were done on a Beowulf

cluster with 92 processors (2.4 GHz)

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Functions and Terminals

Turns• Hard Left, Hard Right, Shallow Left,

Shallow Right, Wings Level, No ChangeSensors• Amplitude > 0, Amplitude Slope < 0,

Amplitude Slope > 0, AoA <, AoA >Functions• IfThen, IfThenElse, And, Or, Not, <, =<,

>, >=, > 0, < 0, =, +, -, *, /

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Fitness Functions

Normalized distance• UAV’s flight to vicinity of the radar

Circling distance• Distance from UAV to radar when in-range

Level time• Time with a roll angle of zero

Turn cost• Changes in roll angle greater than 10°

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Normalized Distance

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fitness

idistance

distance

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at time distance theis

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distance initial theis

steps timeofnumber total theis

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Circling Distance

2

i

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iii2

fitness

in_range

N

distancein_rangeN

fitness

minimize tois goal The

otherwise 0 and range,-in is UAV when the1 is

target theof range-in steps timeofnumber theis

)(*1

1

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Level Time

3

i

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fitness

level

levelin_rangefitness

maximize tois goal The

otherwise 0 and level, are wings the when1 is

*)1(1

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Turn Cost

4

i

i

T

i1iii4

fitness

iroll_angle

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roll_angleroll_angleh_turnT

fitness

minimize tois goal The

at time UAV theof angle roll theis

10 than moreby changed has angle roll theif 1 is

*1

1

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Performance of Evolution

• Multi-objective genetic programming produces a Pareto front of solutions, not a single best solution.

• To gauge the performance of evolution, fitness values for each fitness measure were selected for a minimally successful controller.

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Baseline Values

Normalized Distance ≤ 0.15• Determined empirically

Circling Distance ≤ 4• Average distance less than 2 nmi

Level Time ≥ 1000• ~50% of time (not in-range) with roll angle = 0

Turn Cost ≤ 0.05• Turn sharply less than 0.5% of the time

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Experiments

Continuously emitting, stationary radar• Simplest radar case

Intermittently emitting, stationary radar• Period of 10 minutes, duration of 5 minutes

Continuously emitting, mobile radar• States: move, setup, deployed, tear down• In deployed over an hour before moving again

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Results

Radar TypeRuns Controllers

Total Succ. Rate Total Avg. Max.

Continuously emitting, stationary radar 50 45 90% 3,149 62.98 170

Intermittently emitting, stationary radar 50 25 50% 1,891 37.82 156

Continuously emitting, mobile radar 50 36 72% 2,266 45.32 206

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Continuously emitting, stationary radar

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Circling Behavior

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Intermittently emitting, stationary radar

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Continuously emitting, mobile radar

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Conclusions

• Autonomous navigation controllers were evolved to fly to a radar and then circle around it while maintaining stable and efficient flight dynamics

• Multi-objective genetic programming was used to evolve controllers

• Controllers were evolved for three radar types

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Future Work

Accomplished• Incremental evolution was used to aid in

the evolution of controllers for more complex radar types and controllers able to handle all radar types

• Controllers were successfully tested on a wheeled mobile robot equipped with an acoustic array tracking a speaker

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Future Work

In Progress• Distributed multi-agent controllers will

be evolved to deploy multiple UAVs to multiple radars

• Controllers will be tested on physical UAVs for several radar types in field tests next year

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Acknowledgements

• This work was done at North Carolina State University and the U.S. Naval Research Laboratory

• Financial support was provided by the Office of Naval Research

• Computational resources were provided by the U.S. Naval Research Laboratory