Choong K' Oh and Gregory J' Barlow

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Autonomous Controller Design for Unmanned Aerial
Vehicles using Multi-objective Genetic Programming

• Choong K. Oh and Gregory J. Barlow
• U.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
Sensors
Evolved Controller
Roll angle
UAV Flight
Autopilot
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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 Change
• Sensors
• Amplitude gt 0, Amplitude Slope lt 0, Amplitude
  Slope gt 0, AoA lt, AoA gt
• Functions
• IfThen, IfThenElse, And, Or, Not, lt, lt, gt, gt, gt
  0, lt 0, , , -, , /

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Fitness Functions

• Normalized distance
• UAVs 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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Circling Distance
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Level Time
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Turn Cost
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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
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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