Funding Source: Cyber Valley
Motivation: Aeronautics is currently undergoing a major transition worldwide. The pandemic has shown that airline traffic can be drastically reduced with an extremely beneficial effect on atmospheric emissions. At the same time, there are large investments and developments in the field of individual mobility, such as urban air mobility vehicles and unmanned aircraft applications, e.g. for medical transportation or rescue purposes. The majority of these aircraft or aerial vehicles are based on electric propulsion and thus, range and endurance are quite limited. In the case of fixed-wing aircraft, these parameters can be significantly improved by exploiting (harvesting) energy from the atmospheric environment. An extreme example is conventional soaring, which is a glider flight, where a pilot combines experience, skills, knowledge, and perception in a decision-making process in such a way, that updrafts are detected and exploited to maximize flight range while keeping situational awareness at all times. These tasks are very complex and can only be accomplished by highly trained pilots. The objective of this work is to find systematic approaches to autonomously maximize the exploitation of environmental energy for flights with small fixed-wing aircraft (unmanned or manned, <2t), while, at the same time, minimizing the flight duration for a required distance. The underlying problem is the trade of short-term rewarding actions (covering some distance) against actions that are expected to pay off in the long term (mapping and exploiting atmospheric updrafts) while navigating in a complex, particularly hard-to-model environment. This constitutes a challenging decision-making problem. Autonomous soaring serves as a test scenario.
Goals and Objectives:
 Stefan Notter, Fabian Schimpf, Gregor Müller, and Walter Fichter, Hierarchical Reinforcement Learning Approach for Autonomous Cross-Country Soaring, Journal of Guidance, Control, and Dynamics 2023 46:1, 114-126