Autonomous Active Mapping in Steep Alpine Environments with Fixed-wing Aerial Vehicles

Monitoring large scale environments is a crucial task for managing remote alpine environments, especially for hazardous events such as avalanches. One key information for avalanche risk forecast is imagery of released avalanches. As these happen in remote and potentially dangerous locations this data is difficult to obtain. Fixed-wing vehicles, due to their long range and travel speeds are a promising platform to gather aerial imagery to map avalanche activities. However, operating such vehicles in mountainous terrain remains a challenge due to the complex topography, regulations, and uncertain environment. In this work, we present a system that is capable of safely navigating and mapping an avalanche using a fixed-wing aerial system and discuss the challenges arising when executing such a mission. We show in our field experiments that we can effectively navigate in steep terrain environments while maximizing the map quality. We expect our work to enable more autonomous operations of fixed-wing vehicles in alpine environments to maximize the quality of the data gathered.Comment: 8 pages, 8 figures, Accepted to the IEEE ICRA Workshop on Field Robotics 202

Safe Low-Altitude Navigation in Steep Terrain with Fixed-Wing Aerial Vehicles

Fixed-wing aerial vehicles provide an efficient way to navigate long distances or cover large areas for environmental monitoring applications. By design, they also require large open spaces due to limited maneuverability. However, strict regulatory and safety altitude limits constrain the available space. Especially in complex, confined, or steep terrain, ensuring the vehicle does not enter an inevitable collision state(ICS) can be challenging. In this work, we propose a strategy to find safe paths that do not enter an ICS while navigating within tight altitude constraints. The method uses periodic paths to efficiently classify ICSs. A sampling-based planner creates collision-free and kinematically feasible paths that begin and end in safe periodic (circular) paths. We show that, in realistic terrain, using circular periodic paths can simplify the goal selection process by making it yaw agnostic and constraining yaw. We demonstrate our approach by dynamically planning safe paths in real-time while navigating steep terrain on a flight test in complex alpine terrain.Comment: Accepted to IEEE Robotics and Automation Letters (RA-L

WindSeer: Real-time volumetric wind prediction over complex terrain aboard a small UAV

Real-time high-resolution wind predictions are beneficial for various applications including safe manned and unmanned aviation. Current weather models require too much compute and lack the necessary predictive capabilities as they are valid only at the scale of multiple kilometers and hours - much lower spatial and temporal resolutions than these applications require. Our work, for the first time, demonstrates the ability to predict low-altitude wind in real-time on limited-compute devices, from only sparse measurement data. We train a neural network, WindSeer, using only synthetic data from computational fluid dynamics simulations and show that it can successfully predict real wind fields over terrain with known topography from just a few noisy and spatially clustered wind measurements. WindSeer can generate accurate predictions at different resolutions and domain sizes on previously unseen topography without retraining. We demonstrate that the model successfully predicts historical wind data collected by weather stations and wind measured onboard drones

Molecular spintronics: Coherent spin transfer in coupled quantum dots

Time-resolved Faraday rotation has recently demonstrated coherent transfer of electron spin between quantum dots coupled by conjugated molecules. Using a transfer Hamiltonian ansatz for the coupled quantum dots, we calculate the Faraday rotation signal as a function of the probe frequency in a pump-probe setup using neutral quantum dots. Additionally, we study the signal of one spin-polarized excess electron in the coupled dots. We show that, in both cases, the Faraday rotation angle is determined by the spin transfer probabilities and the Heisenberg spin exchange energy. By comparison of our results with experimental data, we find that the transfer matrix element for electrons in the conduction band is of order 0.08 eV and the spin transfer probabilities are of order 10%.Comment: 13 pages, 6 figures; minor change

Spectral focusing of broadband silver electroluminescence in nanoscopic FRET-LEDs

Few inventions have shaped the world like the incandescent bulb. Edison used thermal radiation from ohmically heated conductors, but some noble metals also exhibit ‘cold’ electroluminescence in percolation films1,2, tunnel diodes3, electromigrated nanoparticle aggregates4,5, optical antennas6 or scanning tunnelling microscopy7,8,9. The origin of this radiation, which is spectrally broad and depends on applied bias, is controversial given the low radiative yields of electronic transitions. Nanoparticle electroluminescence is particularly intriguing because it involves localized surface-plasmon resonances with large dipole moments. Such plasmons enable very efficient non-radiative fluorescence resonance energy transfer (FRET) coupling to proximal resonant dipole transitions. Here, we demonstrate nanoscopic FRET–light-emitting diodes which exploit the opposite process, energy transfer from silver nanoparticles to exfoliated monolayers of transition-metal dichalcogenides10. In diffraction-limited hotspots showing pronounced photon bunching, broadband silver electroluminescence is focused into the narrow excitonic resonance of the atomically thin overlayer. Such devices may offer alternatives to conventional nano-light-emitting diodes11 in on-chip optical interconnects

Perceiving the air for safer and more efficient fixed-wing UAV flights

Recent advances in small uncrewed aerial vehicle (sUAV) technology has caused a surge in using sUAVs in a wide range of scientific and commercial applications. Specifically, fixed-wing and hybrid sUAVs allow large-scale beyond visual line of sight (BVLOS) missions, e.g. distributing medical goods to isolated locations or remote glacier monitoring. Current BVLOS regulations require low altitude operations where the wind can exhibit complex flow patterns and chaotic velocity changes that pose safety risks to sUAVs. On the other side, the wind and thermals, pockets of hot rising air caused by temperature variations on the ground, can be exploited to extend the range and flight time of soaring sUAVs. However, current sUAVs lack the ability to remotely predict the wind and potential thermal locations. The primary goal of this thesis is to develop efficient algorithms to remotely perceive the air, specifically the low-altitude wind around terrain and thermal updrafts, for safer and more efficient sUAV flight. In Part A we tackle the challenge of predicting the dense low-altitude wind around complex terrain. We trained deep neural networks (DNNs) to predict the wind using computational fluid dynamics (CFD)-simulated flows over realistic terrain patches. In a first version, the input to the DNN is composed of the known elevation map and wind inflow condition. We demonstrated that these wind predictions allow planning for safer and more efficient flight paths. As the inflow condition is not available onboard during flight we developed a second DNN, WindSeer, to predict the wind and turbulence based on the elevation map and sparse wind measurements. These sparse measurements can be collected onboard an sUAV during flight. We demonstrate zero-shot sim-to-real transfer by evaluating WindSeer, trained only with CFD simulated data, on real wind data without retraining. WindSeer accurately predicts the wind and turbulence collected at weather stations across different hills in Europe and can accurately reconstruct the wind given onboard measurements as validated by data from multiple fixed-wing sUAVs. In Part B we address the challenge of predicting and detecting remote thermal updraft locations. We first enabled building a temperature map of the ground by developing MultiPoint, a DNN-based keypoint detector and descriptor for the optical and thermal infrared (TIR) spectrum. We composed a dataset of aligned optical-TIR image pairs using a mutual information (MI)-based pipeline. Multi- Point, trained in a multi-stage pipeline in a self-supervised fashion, significantly outperforms baseline detectors and descriptors on multi-spectral image data. In a next step, we demonstrated in a proof of concept that DNNs are able to detect schlieren, brightness and color changes due to refractive index gradients in air, using a single greyscale image. We first collect a dataset of schlieren optical flows in an ideal indoor lab setting labelled with background oriented schlieren (BOS) methods. Then we trained the DNN using the labelled flows with a mix of real imagery and synthetically generated images. Finally, we showed the performance of the model on held back data and real-world imagery. Overall, in this thesis we presented DNN-based methods able to run onboard an sUAV perceiving the air to enable planning for safer and more efficient flight paths for BVLOS missions. We conclude the thesis with suggestions to extend this work

Safe Low-Altitude Navigation in Steep Terrain With Fixed-Wing Aerial Vehicles

ISSN:2377-376

Circling Back: Dubins set Classification Revisited

Dubins paths are commonly used in robot motion planning for generating minimal-length fixed-curvature motions between two states. Existing analytical approaches generate the Dubins set - the set of paths consisting of different sequences of arcs and straight lines that contains the optimal solution for travelling between a state pair. Typically, the length for each path in the set is evaluated and the shortest path is selected. Dubins set classification approaches use an additional pre-calculation phase to further reduce the Dubins set before evaluating path length. This can significantly reduce computational costs, especially for sampling based planners in the Dubins space that perform many path length evaluations during a search. This paper addresses the issue of degenerate solutions from the Dubins set classification method presented in~\cite{shkel_classication_2001} when solving for a shortest path using Dubins paths. The results show that a Dubins set classification approach can result in 58$\%$ reduced computation time for computing a Dubins path but still return the optimal path when compared to evaluating the full Dubins set