Extending Mission Duration of UAS Multicopters: Multi-disciplinary Approach

Multicopters are important tools in industry, the military, and research but suffer from short flight times and mission durations. In this thesis, we discuss three different ways to increase flight times and therefore increase the viability of using multicopters in a variety of missions. Alternate fuel sources such as hydrogen fuel and solar cells are starting to be used on multicopters, in our research we simulate modern fuel cells and show how well they currently work as the power source for multicopters and how close they are to becoming useful in Unmanned Aircraft System (UAS) technology. Increasing the efficiency in which the available energy is used can also increase mission duration. Two characteristics that affect the efficiency of a mission are the flight speeds of the multicopter and the payload it carries. These characteristics are well known in larger rotorcrafts but often ignored in smaller multicopters. In our research, we explore the effect of flight speed on the dynamics of a multicopter and show that higher speeds lead to higher flight times due to the effect of translational lift. Lastly, we developed an online updating multi-flight planning algorithm for stop and charge missions, a method that can potentially indefinitely extend a mission. The multi-flight planning algorithm, the variable resolution horizon, reduces the computing resources necessary to 15% to 40% of a typical optimal planner while having a maximum 5.6% decrease in expected future reward, a metric for accuracy. The results of this thesis help guide decisions in fuel type for multicopter missions show examples of how to increase flight time through increasing efficiency and develop the framework for multi-flight missions. Advisers: Justin Bradley and Carrick Detweile

Mobile Robotics Planning using Abstract Markov Decision Processes

Colloque avec actes et comité de lecture.Markov Decision Processes have been successfully used in robotics for indoor robot navigation problems. They allow to compute optimal sequences of actions in order to achieve a given goal, accounting for actuators uncertainties. But MDPs are weak to avoid unknown obstacles. At the opposite reactive navigators are particulary a dapted to that, and don't need any prior knowledge about the environment. But they are unable to plan the set of actions that will permit the realization of a given mission. We present a new state aggregation technique for Markov Decision Processes, such that part of the work usually dedicated to the planner is achieved by a reactive navigator. Thus some characteristics of our environments, such as width of corridors, have not to be considered, which allows to cluster states together, si gnificantly reducing the state space. As a consequence, policies are computed faster and are shown to be at least as efficient as optimal ones