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Incremental Evolution of Autonomous Controllers for
Unmanned Aerial Vehicles using Multi-objective Genetic
Programming
Gregory J. Barlow, Choong K. Oh, and Edward GrantNorth Carolina State University
U.S. Naval Research Laboratory
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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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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
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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
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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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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 run had a population size of 500• 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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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
Intermittently emitting, mobile radar• Most difficult radar type for evolution
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Direct Evolution
Radar TypeRuns Controllers
Total Succ. Rate Total Avg. Max.
Continuously emitting, stationary radar 50 45 90% 3,149 63 170
Continuously emitting, mobile radar 50 36 72% 2,266 45.3 206
Intermittently emitting, stationary radar 50 25 50% 1,891 37.8 156
Intermittently emitting, mobile radar 50 16 32% 569 11.38 93
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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 TypeRuns Controllers
Total Succ. Rate Total Avg. Max.
Continuously emitting, stationary radar 50 45 90% 2,815 56.30 166
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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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
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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