slide1
Download
Skip this Video
Download Presentation
Gregory J. Barlow, Choong K. Oh, and Edward Grant North Carolina State University

Loading in 2 Seconds...

play fullscreen
1 / 25

Gregory J. Barlow, Choong K. Oh, and Edward Grant North Carolina State University - PowerPoint PPT Presentation


  • 88 Views
  • Uploaded on

Gregory J. Barlow, Choong K. Oh, and Edward Grant North Carolina State University U.S. Naval Research Laboratory. Incremental Evolution of Autonomous Controllers for Unmanned Aerial Vehicles using Multi-objective Genetic Programming. Overview. Problem Unmanned Aerial Vehicle Simulation

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about ' Gregory J. Barlow, Choong K. Oh, and Edward Grant North Carolina State University' - blossom-martinez


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
slide1
Gregory J. Barlow, Choong K. Oh, and Edward Grant

North Carolina State University

U.S. Naval Research Laboratory

Incremental Evolution of Autonomous Controllers for Unmanned Aerial Vehicles using Multi-objective Genetic Programming

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

We have previously evolved 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
problem
Problem
  • We are most interested in the more difficult radar types, particularly intermittently emitting, mobile radars
  • Evolving controllers directly on the most difficult radars yields very low rates of success
  • We would like to create controllers able to handle all of the radar types rather than having one controller for each type
simulation
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
sensors
Sensors
  • UAVs can sense the angle of arrival (AoA) and amplitude of incoming radar signals
uav control
UAV Control

Sensors

Evolved

Controller

Roll angle

UAV

Flight

Autopilot

transference
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°
multi objective gp
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 run had a population size of 500
  • Computations were done on a Beowulf cluster with 92 processors (2.4 GHz)
functions and terminals
Functions and Terminals

Turns

  • Hard Left, Hard Right, Shallow Left, Shallow Right, Wings Level, No Change

Sensors

  • Amplitude > 0, Amplitude Slope < 0, Amplitude Slope > 0, AoA <, AoA >

Functions

  • IfThen, IfThenElse, And, Or, Not, <, =<, >, >=, > 0, < 0, =, +, -, *, /
fitness functions
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°
performance of evolution
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.
baseline values
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
experiments
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 beforemoving again

Intermittently emitting, mobile radar

  • Most difficult radar type for evolution
incremental evolution
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
conclusions
Conclusions
  • Autonomous navigation controllers were evolved to fly to a radar and then circle around it while maintaining stable and efficient flight dynamics
  • Using incremental evolution dramatically increased the chances of producing successful controllers
  • Incremental evolution produced controllers able to handle all radar types
future work
Future Work
  • We have successfully tested evolved controllers on a wheeled mobile robot equipped with an acoustic array tracking a speaker
  • Controllers will be tested on physical UAVs for several radar types in field tests next year
  • Distributed multi-agent controllers will be evolved to deploy multiple UAVs to multiple radars
ad