IN UAV SWARMS FOR OPTIMIZATION IN COMBAT ENVIRONMENTS

AFIT/GCS/ENG/08-18 This investigation focuses primarily on the development of effective target en-gagement for unmanned aerial vehicle (UAV) swarms using autonomous self-organized cooperative control. This development required the design of a new abstract UAV swarm control model which flows from an abstract Markov structure, a Partially Observable Markov Decision Process. Self-organization features, bio-inspired attack concepts, evolutionary computation (multi-objective genetic algorithms, differential evolution), and feedback from environmental awareness are instantiated within this model. The associated decomposition technique focuses on the iterative deconstruc-tion of the problem domain state and dynamically building-up of self organizational rules as related to the problem domain environment. Resulting emergent behaviors provide the appropriate but different dynamic activity of each UAV agent for sta-tistically accomplishing the required multi-agent temporal attack task. The current application implementing this architecture involves both UAV flight formation behav