Autonomous Controller Design for Unmanned Aerial Vehicles using Multi-objective Genetic Programming
Gregory J. Barlow

Autonomous navigation controllers were developed for fixed wing unmanned
aerial vehicle (UAV) applications using multi-objective genetic
programming (GP). Four fitness functions derived from flight simulations
were designed and multi-objective GP was used to evolve controllers able
to locate a radar source, navigate the UAV to the source efficiently using
on-board sensor measurements, and circle around the emitter. Controllers
were evolved for three different kinds of radars: stationary, continuously
emitting radars, stationary, intermittently emitting radars, and mobile,
continuously emitting radars. In this study, realistic flight parameters
and sensor inputs were selected to aid in the transference of evolved
controllers to physical UAVs.