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 Evolved Controller Autopilot UAV Flight Sensors Roll angle

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 > 0, Amplitude Slope 0, AoA Functions IfThen, IfThenElse, And, Or, Not,, >=, > 0, < 0, =, +, -, *, /

11 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°

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 RunsControllers TotalSucc.RateTotalAvg.Max. Continuously emitting, stationary radar %3, Intermittently emitting, stationary radar %1, Continuously emitting, mobile radar %2,

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 RunsControllers TotalSucc.RateTotalAvg.Max. Continuously emitting, stationary radar %2, Intermittently emitting, stationary radar %2, Continuously emitting, mobile radar %2, Intermittently emitting, stationary radar %2, Intermittently emitting, mobile radar %1,

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