Evolved Navigation Control for Unmanned Aerial Vehicles
Gregory J. Barlow and Choong K. Oh


Whether evolutionary robotics (ER) controllers evolve in simulation or on 
real robots, real-world performance is the true test of an evolved 
controller. Controllers must overcome the noise inherent in real 
environments to operate robots efficiently and safely. To prevent a poorly 
performing controller from damaging a vehicle—susceptible vehicles 
include statically unstable walking robots, flying vehicles, and 
underwater vehicles—it is necessary to test evolved controllers 
extensively in simulation before transferring them to real robots.  In 
this paper, we present our approach to evolving behavioral navigation 
controllers for fixed wing unmanned aerial vehicles (UAVs) using 
multi-objective genetic programming (GP), choosing the most robust evolved 
controller, and assuring controller performance prior to real flight 
tests.