A priori and on-line route optimization for unmanned underwater vehicles

Abstract

The U.S. military considers Unmanned Underwater Vehicles (UUVs) a critical component of the future for two primary reasons - they are effective force multipliers and a significant risk-reducing agent. As the military's technology improves and UUVs become a reliable mission asset, the vehicle's ability to make intelligent decisions will be crucial to future operations. The thesis develops various algorithms to solve the UUV Mission-Planning Problem (UUVMPP), where the UUV must choose which tasks to perform in which sequence in a stochastic mission environment. The objective is to find the most profitable way to execute tasks with restrictions of total mission time, energy, time-restricted areas, and weather conditions. Since the UUV accumulates navigation error over time while maneuvering underwater, the UUV must occasionally halt operations to re-orient itself via a navigation fix. While a navigation fix takes time and increases the likelihood of exposing the vehicle's position to potential adversaries, a reduction in navigation error allows the UUV to perform tasks and navigate with a greater amount of certainty. The algorithms presented in this thesis successfully incorporate navigation fixes into the mission-planning process. The thesis considers Mixed-Integer Programming, Exact Dynamic Programming, and an Approximate Dynamic Programming technique known as Rollout to determine the optimal a priori route that meets operational constraints with a specified probability. The thesis then shows how these formulations can solve and re-solve the UUVMPP on-line. In particular, the Rollout Algorithm finds task route solutions on average 96% of the optimal solution a priori and 98% of the optimal solution on-line compared to exact algorithms; with a significant reduction in computation run time, the Rollout Algorithm permits the solving of increasingly complex mission scenarios.