Using Multiplayer Differential Game Theory to Derive Efficient Pursuit-Evasion Strategies for Unmanned Aerial Vehicles

In recent years, Unmanned Aerial Vehicles (UAVs) have been used extensively in military conflict situations to execute intelligence, surveillance and reconnaissance missions. However, most of the current UAV platforms have limited collaborative capabilities, and consequently they must be controlled individually by operators on the ground. The purpose of the research presented in this thesis is to derive algorithms that can enable multiple UAVs to reason about the movements of multiple ground targets and autonomously coordinate their efforts in real-time to ensure that the targets do not escape. By improving the autonomy of multivehicle systems, the workload placed on the command and control operators is reduced significantly. To derive effective adversarial control algorithms, the adversarial scenario is modeled as a multiplayer differential game. However, due to the inherent computational complexity of multiplayer differential games, three less computationally demanding differential pursuit-evasion game-based algorithms are presented. The purpose of the algorithms is to quickly derive interception strategies for a team of autonomous vehicles. The algorithms are applicable to scenarios with different base assumptions, that is, the three algorithms are meant to complement one another by addressing different types of adversarial problems.Ph.D