Robustness Analysis of Genetic Programming
Controllers for Unmanned Aerial Vehicles
Gregory J. Barlow and Choong K. Oh

While evolving evolutionary robotics controllers for real vehicles is an 
active area of research, most research robots do not require any assurance 
prior to operation that an evolved controller will not damage the vehicle. 
For controllers evolved in simulation where testing a poorly performing 
controller might damage the vehicle, thorough testing in simulation - 
subject to multiple sources of sensor and state noise - is required. 
Evolved controllers must be robust to noise in the environment in order to 
operate the vehicle safely. We have evolved navigation controllers for 
unmanned aerial vehicles in simulation using multi-objective genetic 
programming, and in order to choose the best evolved controller and to 
assure that this controller will perform well under a variety of 
environmental conditions, we have performed a series of robustness tests. 
The results show that our best evolved controller outperforms two 
hand-designed controllers and is robust to many sources of noise.