Thomas Stastny

Marco Job, Thomas Stastny, Eleni Kelasidi et al.

Autonomous field robots operating in unstructured environments require robust perception to ensure safe and reliable operations. Recent advances in monocular depth estimation have demonstrated the potential of low-cost cameras as depth sensors; however, their adoption in field robotics remains limited due to the absence of reliable scale cues, ambiguous or low-texture conditions, and the scarcity of large-scale datasets. To address these challenges, we propose a depth completion model that trains on synthetic data and uses extremely sparse measurements from depth sensors to predict dense metric depth in unseen field robotics environments. A synthetic dataset generation pipeline tailored to field robotics enables the creation of multiple realistic datasets for training purposes. This dataset generation approach utilizes textured 3D meshes from Structure from Motion and photorealistic rendering with novel viewpoint synthesis to simulate diverse field robotics scenarios. Our approach achieves an end-to-end latency of 53 ms per frame on a Nvidia Jetson AGX Orin, enabling real-time deployment on embedded platforms. Extensive evaluation demonstrates competitive performance across diverse real-world field robotics scenarios.

Thomas Stastny

L1 guidance logic is one of the most widely used path following controllers for small fixed-wing unmanned aerial vehicles (UAVs), primarily due to its simplicity (low-cost implementation on embedded on-board processors, e.g. micro-controllers) and ability to track both circles and lines, which make up the vast majority of a typical fixed-wing vehicle's flight plan. The logic was later extended for speed independent dynamic similarity with an adaptive L1 distance, a formulation currently used on common open-source autopilot platforms. Two primary drawbacks remain, specific to small, slow flying fixed-wing UAVs; namely, 1) the combination of low operator defined gains and high ground speeds may violate the bounds of the algorithms convergence region for the case of loiter circles with small radii and 2) L1 logic breaks down when wind speeds exceed the vehicle's airspeed, another common predicament for small, slow-flying UAVs. This brief presents simple extensions to this extensively field tested algorithm, allowing legacy operators to keep existing controller tunings while taking advantage of the enhanced performance and safety features developed within.

Florian Achermann, Thomas Stastny, Bogdan Danciu et al.

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.

Daniel Hentzen, Thomas Stastny, Roland Siegwart et al.

Many multirotor Unmanned Aerial Systems applications have a critical need for precise position control in environments with strong dynamic external disturbances such as wind gusts or ground and wall effects. Moreover, to maximize flight time, small multirotor platforms have to operate within strict constraints on payload and thus computational performance. In this paper, we present the design and experimental comparison of Model Predictive and PID multirotor position controllers augmented with a disturbance estimator to reject strong wind gusts up to 12 m/s and ground effect. For disturbance estimation, we compare Extended and Unscented Kalman filtering. In extensive in- and outdoor flight tests, we evaluate the suitability of the developed control and estimation algorithms to run on a computationally constrained platform. This allows to draw a conclusion on whether potential performance improvements justify the increased computational complexity of MPC for multirotor position control and UKF for disturbance estimation.

Thomas Stastny, Roland Siegwart

Small, low-speed fixed-wing Unmanned Aerial Vehicles (UAVs) operating autonomously, beyond-visual-line-of-sight (BVLOS) will inevitably encounter winds rising to levels near or exceeding the vehicles' nominal airspeed. In this paper, we develop a nonlinear lateral-directional path following guidance law with explicit consideration of online wind estimates. Energy efficient airspeed reference compensation logic is developed for excess wind scenarios (i.e. when the wind speed rises above the airspeed), enabling either mitigation, prevention, or over-powering of excess wind induced run-away from a given path. The developed guidance law is demonstrated on a representative small, low-speed test UAV in two flight experiments conducted in mountainous regions of Switzerland with strong, turbulent wind conditions, gusts reaching up to 13 meters per second. We demonstrate track-keeping errors of less than 1 meter consistently maintained during a representative duration of gusting, excess winds and a mean ground speed undershoot of 0.5 meters per second from the commanded minimum forward ground speed demonstrated in over 5 minutes of the showcased flight results.

Timo Hinzmann, Thomas Stastny, Cesar Cadena et al.

Full autonomy for fixed-wing unmanned aerial vehicles (UAVs) requires the capability to autonomously detect potential landing sites in unknown and unstructured terrain, allowing for self-governed mission completion or handling of emergency situations. In this work, we propose a perception system addressing this challenge by detecting landing sites based on their texture and geometric shape without using any prior knowledge about the environment. The proposed method considers hazards within the landing region such as terrain roughness and slope, surrounding obstacles that obscure the landing approach path, and the local wind field that is estimated by the on-board EKF. The latter enables applicability of the proposed method on small-scale autonomous planes without landing gear. A safe approach path is computed based on the UAV dynamics, expected state estimation and actuator uncertainty, and the on-board computed elevation map. The proposed framework has been successfully tested on photo-realistic synthetic datasets and in challenging real-world environments.

Thomas Stastny, Roland Siegwart

As off-the-shelf (OTS) autopilots become more widely available and user-friendly and the drone market expands, safer, more efficient, and more complex motion planning and control will become necessary for fixed-wing aerial robotic platforms. Considering typical low-level attitude stabilization available on OTS flight controllers, this paper first develops an approach for modeling and identification of the control augmented dynamics for a small fixed-wing Unmanned Aerial Vehicle (UAV). A high-level Nonlinear Model Predictive Controller (NMPC) is subsequently formulated for simultaneous airspeed stabilization, path following, and soft constraint handling, using the identified model for horizon propagation. The approach is explored in several exemplary flight experiments including path following of helix and connected Dubins Aircraft segments in high winds as well as a motor failure scenario. The cost function, insights on its weighting, and additional soft constraints used throughout the experimentation are discussed.