An Information Value Approach to Route Planning for UAV Search and Track Missions

This dissertation has three contributions in the area of path planning for Unmanned Aerial Vehicle (UAV) Search And Track (SAT) missions. These contributions are: (a) the study of a novel metric, G, used to quantify the value of the target information gained during a search and track mission, (b) an optimal planning horizon that minimizes time-error of a planning horizon when interrupted by Poisson random events, and (c) a modified Particle Swarm Optimization (PSO) algorithm for search missions that uses the prior target distribution in the generation of paths rather than just in the evaluation of them. UAV route planning is an important topic with many applications. Of these, military applications are the best known. This dissertation focuses on route planning for SAT missions that jointly optimize the conflicting objectives of detecting new targets and monitoring previously detected targets. The information theoretic approach proposed here is different from and is superior to existing approaches. One of the main differences is that G quantifies the value of the target information rather than the information itself. Several examples are provided to highlight G’s desirable properties. Another important component of path planning is the selection of a planning horizon, which specifies the amount of time to include in a plan. Unfortunately, little research is available to aid in the selection of a planning horizon. The proposed planning horizon is derived in the context of plan updates triggered by Poisson random events. To our knowledge, it is the only theoretically derived horizon available making it an important contribution. While the proposed horizon is optimal in minimizing planning time errors, simulation results show that it is also near optimal in minimizing the average time needed to capture an evasive target. The final contribution is the modified PSO. Our modification is based on the idea that PSO should be provided with the target distribution for path generation. This allows the algorithm to create candidate path plans in target rich regions. The modified PSO is studied using a search mission and is used in the study of G