Gregory%20J.%20Barlow

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

• Gregory J. Barlow
• 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
Radar Type Runs Runs Runs Controllers Controllers Controllers
Radar Type 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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Incremental Evolution

• Environmental incremental evolution was used to
  improve the success rate for evolving controllers
• A population is evolved on progressively more
  difficult radar types

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Incremental Evolution
Radar Type Runs Runs Runs Controllers Controllers Controllers
Radar Type Total Succ. Rate Total Avg. Max.
Continuously emitting, stationary radar 50 45 90 2,815 56.30 166
Intermittently emitting, stationary radar 50 34 64 2,526 50.52 184
Continuously emitting, mobile radar 50 45 90 2,774 55.48 179
Intermittently emitting, stationary radar 50 42 84 2,083 41.66 143
Intermittently emitting, mobile radar 50 37 74 1,602 32.04 143
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Intermittently emitting, mobile radar
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Transference to a wheeled mobile robot

• Controllers were designed for UAVs
• A UAV was not yet available for flight tests to
  evaluate transference
• Evolved controllers were tested on a wheeled
  mobile robot, the EvBot II
• A speaker was used in place of the radar, and an
  acoustic array in place of the radar sensor

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EvBot II

• PC/104 processor
• Communications with a wireless network card
• Runs Linux
• On-board acoustic array

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Considerations

• In simulation, the sensor accuracy was 10, but
  the acoustic array accuracy was approximately
  45
• Wheeled robot not controlled by roll angle, must
  be turned and then moved
• The size of the maze environment was not
  equivalent to the simulation environment, instead
  the scale size of the maze environment was 1.13
  by 0.9 nautical miles

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Transference
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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 with Dr. Choong Oh at the U.S.
  Naval Research Laboratory and Dr. Edward Grant at
  North Carolina State University
• Financial support was provided by the Office of
  Naval Research
• Computational resources were provided by the U.S.
  Naval Research Laboratory

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Future Concerns

• Evolving complex behaviors
• Communication between UAVs
• Transference to physical UAVs
• Maintaining diversity in the population when
  using incremental evolution