Decision algorithms for Unmanned Underwater Vehicles during offensive operations

Abstract

The field of research involving autonomous vehicles has expanded greatly over the past decade. This thesis addresses the case of a system of Unmanned Underwater Vehicles (UUVs) operating in littoral areas in an offensive capacity. A series of complementary algorithms were designed to collect information about an enemy vessell, and subsequently use this information to both select and move to a prefered intercept location that maximizes the opportunity to both re-acquire and destroy an enemy vessel. Additionally, within the context of a specifically designed simulation, key parameter changes were analyzed to determine their effectiveness to improve the system's performance as measured by four measures of effectiveness. A methodology was also designed to optimize the location of the engaging UUVs to maximize their effectiveness, and capitalize on the enemy movement within the operational area. Results are presented for both original locations and optimized locations, and initial findings provide insight into the effectiveness of the designed algorithms and statistical inference of these key parameter changes.