Title: Gregory%20J.%20Barlow
1Autonomous Controller Design for Unmanned Aerial
Vehicles using Multi-objective Genetic Programming
- Gregory J. Barlow
- North Carolina State University
2Overview
- Problem
- Unmanned Aerial Vehicle Simulation
- Multi-objective Genetic Programming
- Fitness Functions
- Experiments and Results
- Conclusions
- Future Work
3Problem
- 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
4Controller 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
5Simulation
- 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
6Sensors
- UAVs can sense the angle of arrival (AoA) and
amplitude of incoming radar signals
7UAV Control
Sensors
Evolved Controller
Roll angle
UAV Flight
Autopilot
8Transference
- 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
9Multi-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)
10Functions 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, , , -, , /
11Fitness 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
12Normalized Distance
13Circling Distance
14Level Time
15Turn 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.
17Baseline 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
18Experiments
- 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
19Results
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
20Continuously emitting, stationary radar
21Circling Behavior
22Intermittently emitting, stationary radar
23Continuously emitting, mobile radar
24Conclusions
- 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
25Future 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
26Incremental Evolution
- Environmental incremental evolution was used to
improve the success rate for evolving controllers - A population is evolved on progressively more
difficult radar types
27Incremental 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
28Intermittently emitting, mobile radar
29Transference 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
30EvBot II
- PC/104 processor
- Communications with a wireless network card
- Runs Linux
- On-board acoustic array
31Considerations
- 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
32Transference
33Future 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
34Acknowledgements
- 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
35Future Concerns
- Evolving complex behaviors
- Communication between UAVs
- Transference to physical UAVs
- Maintaining diversity in the population when
using incremental evolution