Mechanical & Materials Engineering, Department of
First Advisor
Justin Bradley
Date of this Version
5-2024
Citation
A thesis presented to the faculty of the Graduate College at the University of Nebraska in partial fulfillment of requirements for the degree of Master of Science
Major: Mechanical Engineering and Applied Mechanics
Under the supervision of Professor Justin Bradley
Lincoln, Nebraska, May 2024
Abstract
Mission and flight planning problems for uncrewed aircraft systems (UASs) are typically large and complex in space and computational requirements. With enough time and computing resources, some of these problems may be solvable offline and then executed during flight. In dynamic or uncertain environments, however, the mission may require online adaptation and replanning. In this work, we will discuss methods of creating MDPs for online applications, and a method of using a sliding resolution and receding horizon approach to build and solve Markov Decision Processes (MDPs) in practical planing applications for UASs. In this strategy, called a Sliding Markov Decision Processes (SMDP), the underlying state space is regularly rediscretized according to its informational proximity and utility while a receding horizon algorithm allows us to consider immediate next steps while keeping the primary goal state in mind. This approach allows for dynamic decision making and replanning by a UAS in an uncertain and dynamic environment in which mission objectives or the environment could change. The SMDP method shows an ability to create recursively optimal policies, under conditions of limited computing power and time, that perform similarly to the optimal policy of the associated fully-modeled flat MDP.
Advisor: Justin Bradley
Included in
Artificial Intelligence and Robotics Commons, Materials Science and Engineering Commons, Mechanical Engineering Commons, Robotics Commons
Comments
Copyright 2024, Trent Wiens. Used by permission