Robotic technologies for solar‐powered UAVs: Fully autonomous updraft‐aware aerial sensing for multiday search‐and‐rescue missions

Oettershagen, Philipp; Stastny, Thomas; Hinzmann, Timo; Rudin, Konrad; Mantel, Thomas; Melzer, Amir; Wawrzacz, Bartosz; Hitz, Gregory; Siegwart, Roland

doi:10.1002/rob.21765

Cited by 40 publications

(30 citation statements)

References 36 publications

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“…Creating orthomosaic images differs to ordinary image stitching as it transforms perspectives to the nadir direction (a top-down view orthogonal to a horizontal plane) and, more importantly, performs true-to-scale operations in which an image pixel corresponds to a metric unit [37,38]. This procedure consists of three key steps: (1) initial processing, (2) point densification, and (3) DSM and orthomosaic generation.…”

Section: Orthomosaic Map Generationmentioning

confidence: 99%

WeedMap: A Large-Scale Semantic Weed Mapping Framework Using Aerial Multispectral Imaging and Deep Neural Network for Precision Farming

et al. 2018

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The ability to automatically monitor agricultural fields is an important capability in precision farming, enabling steps towards more sustainable agriculture. Precise, high-resolution monitoring is a key prerequisite for targeted intervention and the selective application of agro-chemicals. The main goal of this paper is developing a novel crop/weed segmentation and mapping framework that processes multispectral images obtained from an unmanned aerial vehicle (UAV) using a deep neural network (DNN). Most studies on crop/weed semantic segmentation only consider single images for processing and classification. Images taken by UAVs often cover only a few hundred square meters with either color only or color and near-infrared (NIR) channels. Although a map can be generated by processing single segmented images incrementally, this requires additional complex information fusion techniques which struggle to handle high fidelity maps due to their computational costs and problems in ensuring global consistency. Moreover, computing a single large and accurate vegetation map (e.g., crop/weed) using a DNN is non-trivial due to difficulties arising from: (1) limited ground sample distances (GSDs) in high-altitude datasets, (2) sacrificed resolution resulting from downsampling high-fidelity images, and (3) multispectral image alignment. To address these issues, we adopt a stand sliding window approach that operates on only small portions of multispectral orthomosaic maps (tiles), which are channel-wise aligned and calibrated radiometrically across the entire map. We define the tile size to be the same as that of the DNN input to avoid resolution loss. Compared to our baseline model (i.e., SegNet with 3 channel RGB (red, green, and blue) inputs) yielding an area under the curve (AUC) of [background=0.607, crop=0.681, weed=0.576], our proposed model with 9 input channels achieves [0.839, 0.863, 0.782]. Additionally, we provide an extensive analysis of 20 trained models, both qualitatively and quantitatively, in order to evaluate the effects of varying input channels and tunable network hyperparameters. Furthermore, we release a large sugar beet/weed aerial dataset with expertly guided annotations for further research in the fields of remote sensing, precision agriculture, and agricultural robotics.

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Section: Orthomosaic Map Generationmentioning

confidence: 99%

WeedMap: A Large-Scale Semantic Weed Mapping Framework Using Aerial Multispectral Imaging and Deep Neural Network for Precision Farming

et al. 2018

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“…The implemented multithreaded framework combines a light-weight, real-time frontend with a backend which is periodically updated based on the host's resources. The linear approach path, which is one output of our method, can be tracked as demonstrated in [1]. The actual landing attempt should furthermore be supported by a perception system, local re-planners and low-level autopilot logic to avoid previously unmapped or moving obstacles.…”

Section: Discussionmentioning

confidence: 99%

“…The final decision module outputs the landing site location, optimal approach vector, and statistics about the final landing site. The actual tracking of the final approach path is described in [1]. The metrics of the best landing sites are stored to be able to land quickly in the case of an emergency.…”

Section: The Approachmentioning

confidence: 99%

Free LSD: Prior-Free Visual Landing Site Detection for Autonomous Planes

Hinzmann

Stastny

Cadena

et al. 2018

IEEE Robot. Autom. Lett.

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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.

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“…It can be inferred that even the first version of the AtlantikSolar UAV (AS-1, see [29]) provides sufficient feasible launch dates from mid-May to end-July when requiring a state of charge margin of 10 %. Obviously, the later versions of AtlantikSolar (AS-2 and AS-3, see [30] and [31]) would improve the performance metrics. Additional analysis yields a minimum and average flight time of 52 h and 78 h respectively versus the 106 h for the no-wind unit test of Section 3.2.…”

Section: Historical Weather Data: Determining the Seasonal Dependencymentioning

confidence: 99%

Meteorology-Aware Multi-Goal Path Planning for Large-Scale Inspection Missions with Solar-Powered Aircraft

Oettershagen

Förster

Wirth

et al. 2019

Journal of Aerospace Information Systems

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Solar-powered aircraft promise significantly increased flight endurance over conventional aircraft. While this makes them promising candidates for large-scale aerial inspection missions, their structural fragility necessitates that adverse weather is avoided using appropriate path planning methods. This paper therefore presents MetPASS, the Meteorology-aware Path Planning and Analysis Software for Solar-powered UAVs. MetPASS is the first path planning framework in the literature that considers all aspects that influence the safety or performance of solar-powered flight: It avoids environmental risks (thunderstorms, rain, wind, wind gusts and humidity) and exploits advantageous regions (high sun radiation or tailwind). It also avoids system risks such as low battery state of charge and returns safe paths through cluttered terrain. MetPASS imports weather data from global meteorological models, propagates the aircraft state through an energetic system model, and then combines both into a cost function. A combination of dynamic programming techniques and an A*-search-algorithm with a custom heuristic is leveraged to plan globally optimal paths in station-keeping, point-to-point or multi-goal aerial inspection missions with coverage guarantees. A full software implementation including a GUI is provided. The planning methods are verified using three missions of ETH Zurich's AtlantikSolar UAV: An 81-hour continuous solar-powered station-keeping flight, a 4000 km Atlantic crossing from Newfoundland to Portugal, and two multi-glacier aerial inspection missions above the Arctic Ocean performed near Greenland in summer 2017. It is shown that integrating meteorological data has significant advantages and is indispensable for the reliable execution of large-scale solar-powered aircraft missions. For example, the correct selection of launch date and flight path across the Atlantic Ocean decreases the required flight time from 106 hours to only 52 hours.

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Robotic technologies for solar‐powered UAVs: Fully autonomous updraft‐aware aerial sensing for multiday search‐and‐rescue missions

Cited by 40 publications

References 36 publications

WeedMap: A Large-Scale Semantic Weed Mapping Framework Using Aerial Multispectral Imaging and Deep Neural Network for Precision Farming

WeedMap: A Large-Scale Semantic Weed Mapping Framework Using Aerial Multispectral Imaging and Deep Neural Network for Precision Farming

Free LSD: Prior-Free Visual Landing Site Detection for Autonomous Planes

Meteorology-Aware Multi-Goal Path Planning for Large-Scale Inspection Missions with Solar-Powered Aircraft

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