Development and Deployment of a Dynamic Soaring Capable UAV using Reinforcement Learning

Dynamic soaring (DS) is a bio-inspired flight maneuver in which energy can be gained by flying through regions of vertical wind gradient such as the wind shear layer. With reinforcement learning (RL), a fixed wing unmanned aerial vehicle (UAV) can be trained to perform DS maneuvers optimally for a variety of wind shear conditions. To accomplish this task, a 6-degreesof- freedom (6DoF) flight simulation environment in MATLAB and Simulink has been developed which is based upon an off-the-shelf unmanned aerobatic glider. A combination of high-fidelity Reynolds-Averaged Navier-Stokes (RANS) computational fluid dynamics (CFD) in ANSYS Fluent and low-fidelity vortex lattice (VLM) method in Surfaces was employed to build a complete aerodynamic model of the UAV. Deep deterministic policy gradient (DDPG), an actor-critic RL algorithm, was used to train a closed-loop Path Following (PF) agent and an Unguided Energy- Seeking (UES) agent. Several generations of the PF agent were presented, with the final generation capable of controlling the climb and turn rate of the UAV to follow a closed-loop waypoint path with variable altitude. This must be paired with a waypoint optimizing agent to perform loitering DS. The UES agent was designed to perform traveling DS in a fixed wind shear condition. It was proven to extract energy from the wind shear to extend flight time during training but did not accomplish sustainable dynamic soaring. Further RL training is required for both agents. Recommendations on how to deploy an RL agent on a physical UAV are discussed

Pathfinder VI Experimental Payload: Desna

The objective of this project is to design, fabricate and test a fixed wing unmanned aerial vehicle (UAV) that is to be carried in, and deployed from the Pathfinder VI rocket. The UAV, known as Desna, is tasked with being able to carry a Tamarisk 640 75mm thermal imaging camera, and transmit live video footage to a ground station from 8500 feet AGL. Desna must also fit inside Pathfinder VI’s 7.5” diameter, 35” long cargo bay. To accomplish this, Desna’s wing configuration, determined through description matrices and light prototype testing, will consist of a 35” wing that rotates about its center with 11” folding winglets to increase lift and stability. Desna will be constructed from blue high-density foam to allow for cheap, rapid prototyping as well as being light as possible while still being able to survive the G loadings during assent. Desna will fly in Pathfinder VI this June in the Intercollegiate Rocket Engineering Competition as an experimental payload

Development of UAV as a Platform for Current and Future Dynamic Soaring Research

We address the ongoing development of a dynamic soaring (DS) capable unmanned aerial vehicle (UAV) platform optimized for minimal power consumption. This project has been funded by the Embry-Riddle Office of Undergraduate Research through the Ignite program. Dynamic soaring is a bio-inspired flight maneuver in which energy is extracted from the wind shear layer by flying through regions of varying wind speed. The objective of our project is to design an autonomous dynamic soaring flight controller and perform DS with a real-world UAV. Development of this project can be divided into three sub-categories: (1) the UAV platform, (2) flight simulations, and (3) the flight controller. The UAV platform is an FMS Fox Aerobatic Glider, a high aspect-ratio glider with a nose-mounted engine. A flight control system has been crafted to allow us to integrate our DS autopilot. In previous works we have created a 6-degree-of-freedom (6DoF) flight simulation environment in MATLAB and Simulink to develop and test DS flight controllers. The simulator can be adapted to integrate our current UAV by building a variable-fidelity aerodynamic model using computational fluid dynamics (CFD). Finally, we are developing a robust reinforcement-learning (RL) trained artificial intelligence (AI) that will optimize the path of the UAV to minimize power consumption. RL is performed in the simulator and the AI will be deployed on the UAV when complete. This presentation will discuss current progress as well as address challenges we face in the completion of our goals

Development of UAV as a Platform for Current and Future Dynamic Soaring Research

We address the final stages in the development of a dynamic soaring (DS) capable unmanned aerial vehicle (UAV). This project has been funded by the Embry-Riddle Office of Undergraduate Research through the Ignite program. Dynamic soaring is a bio-inspired flight maneuver in which energy is extracted by flying through regions of wind velocity gradient such as the wind shear layer. The objective of our project is to design an autonomous dynamic soaring flight controller through simulation, develop a DS capable UAV platform, and perform DS maneuvers in the real world. For simulation, a 6-degrees-of-freedom (6DoF) flight simulation environment in MATLAB and Simulink has been developed. Using computational fluid dynamics (CFD) a variable-fidelity aerodynamic model was obtained. The UAV platform is an FMS Fox Aerobatic Glider, a high aspect-ratio powered glider with a robust sensor suite and autonomous flight control system. Finally, we are developing a reinforcement-learning (RL) trained artificial intelligence (AI) that will optimize the path of the UAV to minimize power consumption. After completion, the UAV will be capable of testing future DS navigation systems. This presentation will discuss current progress as well as address challenges we face in the completion of our goals

Investigating the Heaviest Halogen: Lessons Learned from Modeling the Electronic Structure of Astatine\u27s Small Molecules

We present a systematic study of electron-correlation and relativistic effects in diatomic molecular species of the heaviest halogen astatine (At) within relativistic single- and multi-reference coupled-cluster approaches and relativistic density functional theory. We establish revised reference \textit{ab initio} data for the ground states of \ce{At2}, \ce{HAt}, \ce{AtAu}, and \ce{AtO+} using a highly accurate relativistic effective core potential model and in-house basis sets developed for accurate modeling of molecules with large spin-orbit effects. Spin-dependent relativistic effects on chemical bonding in the ground state are comparable to the binding energy or even exceed it in \ce{At2}. Electron-correlation effects near the equilibrium internuclear separation are mostly dynamical and can be adequately captured using single-reference CCSD(T). However, bond elongation in \ce{At2} and, especially, \ce{AtO+} results in rapid manifestation of its multi-reference character. While useful for evaluating the spin-orbit effects on the ground-state bonding and properties, the two-component density functional theory lacks predictive power, especially in combination with popular empirically adjusted exchange-correlation functionals. This drawback supports the necessity to develop new functionals for reliable quantum-chemical models of heavy-element compounds with strong relativistic effects