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Autonomous Controller Design for Unmanned Aerial Vehicles using Multi-objective Genetic Programming Gregory J. Barlow North Carolina State University – PowerPoint PPT presentation

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Title: Gregory%20J.%20Barlow


1

Autonomous Controller Design for Unmanned Aerial
Vehicles using Multi-objective Genetic Programming
  • Gregory J. Barlow
  • North Carolina State University

2
Overview
  • Problem
  • Unmanned Aerial Vehicle Simulation
  • Multi-objective Genetic Programming
  • Fitness Functions
  • Experiments and Results
  • Conclusions
  • Future Work

3
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

4
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

5
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

6
Sensors
  • UAVs can sense the angle of arrival (AoA) and
    amplitude of incoming radar signals

7
UAV Control
Sensors
Evolved Controller
Roll angle
UAV Flight
Autopilot
8
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

9
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)

10
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, , , -, , /

11
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

12
Normalized Distance
13
Circling Distance
14
Level Time
15
Turn Cost
16
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.

17
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

18
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

19
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
20
Continuously emitting, stationary radar
21
Circling Behavior
22
Intermittently emitting, stationary radar
23
Continuously emitting, mobile radar
24
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

25
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

26
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

27
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
28
Intermittently emitting, mobile radar
29
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

30
EvBot II
  • PC/104 processor
  • Communications with a wireless network card
  • Runs Linux
  • On-board acoustic array

31
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

32
Transference
33
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

34
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

35
Future Concerns
  • Evolving complex behaviors
  • Communication between UAVs
  • Transference to physical UAVs
  • Maintaining diversity in the population when
    using incremental evolution
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