Exploitation of Self Organization in UAV Swarms for Optimization in Combat Environments; Master's thesis

摘要

This investigation focuses primarily on the development of effective target engagement 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 deconstruction 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 statistically accomplishing the required multi-agent temporal attack task. The current application implementing this architecture involves both UAV flight formation behaviors and UAVs attacking targets in hostile environments. This temporal application has been quite successful in computational simulation (animation) with supporting statistical analysis. The effort reflects a considerable increase in effectiveness of UAV attacks related to a previous work with increased damage and decreased causalities. In the process of developing this capability an innovative paradigm shift in autonomous agent system design evolved. Heretofore, large dimensional agent systems were developed with an a priori fixed structure, usually with emphasis on top-down or bottom-up management, control, and sensor communication. Because of the fixed structure, extension to very large dimensional systems is generally impractical.