Improving automatic cooperation between UAVs through co-evolution
James F. Smith · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2008
A fuzzy logic resource manager (RM) that enables a collection of unmanned aerial vehicles (UAVs) to automatically cooperate to make meteorological measurements will be discussed. The RM renders the UAVs autonomous allowing them to change paths and cooperate without human intervention. Innovations related to the "priority for helping" (PH) fuzzy decision tree (FDT) used by the RM will be discussed. The PH FDT permits three types of automatic cooperation between the UAVs. A subroutine of the communications routing algorithm (CRA) used by the RM is also examined. The CRA allows the UAVs to reestablish communications if needed by changing their behavior. A genetic program (GP) based procedure for automatically creating FDTs is briefly described. A GP is an algorithm based on the theory of evolution that automatically evolves mathematical expressions or computer algorithms. The GP data mines a scenario database to automatically create the FDTs. A recently invented co-evolutionary process that allows improvement of the initially data mined FDT will be discussed. Co-evolution uses a genetic algorithm (GA) to evolve scenarios to augment the GP's scenario database. The GP data mines the augmented database to discover an improved FDT. The process is iterated ultimately evolving a very robust FDT. Improvements to the PH FDT offered through co-evolution are discussed. UAV simulations using the improved PH FDT and CRA are provided.