Flight Path Optimization of a Hang-Glider in a Thermal Updraft

Chudej, Kurt; Klingler, Anna-Lena; Britzelmeier, Andreas

doi:10.1016/j.ifacol.2015.05.076

Cited by 2 publications

(4 citation statements)

References 6 publications

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“…However, they acknowledged that these results are only theoretical upper limits, which will not be easily achievable in the real world, due to a lack of knowledge about thermal activity. Chudej et al [5] created a numerical model of thermal updrafts and implemented these into the three-dimensional equations of motion. Lee et al [24] developed an autonomous soaring controller for an unpowered UAV and applied it in a simulated environment with multiple thermals.…”

Section: Soaring Gliders and Unmanned Aerial Vehiclesmentioning

confidence: 99%

“…Thermals are created by a mass of air rising due to changes in temperature or humidity, in relation to the other surrounding air, and in close proximity to the ground [3]. This parcel of warm air rises due to its lower mass density forming a 'thermal bubble' [5]. At ground level, if the air is homogeneously heated-up or contains the same moisture level, an equilibrium exists.…”

Section: Introductionmentioning

confidence: 99%

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Using Explainable Artificial Intelligence (XAI) to Predict the Influence of Weather on the Thermal Soaring Capabilities of Sailplanes for Smart City Applications

Schnieder

2024

Smart Cities

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Background: Drones, also known as unmanned aerial vehicles, could potentially be a key part of future smart cities by aiding traffic management, infrastructure inspection and maybe even last mile delivery. This paper contributes to the research on managing a fleet of soaring aircraft by gaining an understanding of the influence of the weather on soaring capabilities. To do so, machine learning algorithms were trained on flight data, which was recorded in the UK over the past ten years at selected gliding clubs (i.e., sailplanes). Methods: A random forest regressor was trained to predict the flight duration and a random forest (RF) classifier was used to predict whether at least one flight on a given day managed to soar in thermals. SHAP (SHapley Additive exPlanations), a form of explainable artificial intelligence (AI), was used to understand the predictions given by the models. Results: The best RF have a mean absolute error of 5.7 min (flight duration) and an accuracy of 81.2% (probability of soaring in a thermal on a given day). The explanations derived from SHAP are in line with the common knowledge about the effect of weather systems to predict soaring potential. However, the key conclusion of this study is the importance of combining human knowledge with machine learning to devise a holistic explanation of a machine learning model and to avoid misinterpretations.

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Section: Soaring Gliders and Unmanned Aerial Vehiclesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Using Explainable Artificial Intelligence (XAI) to Predict the Influence of Weather on the Thermal Soaring Capabilities of Sailplanes for Smart City Applications

Schnieder

2024

Smart Cities

View full text Add to dashboard Cite

show abstract

“…Examples include thermal updrafts, mountain waves and shear layers. These structures are approximately steady and predictable enough to be exploited by glider pilots (de Divitiis 2002; Teets & Carter 2002; Langelaan 2007; Chudej, Klingler & Britzelmeier 2015), birds (Ákos et al. 2010; Nourani & Yamaguchi 2017) and autonomous flight vehicles (White et al.…”

Section: Introductionmentioning

confidence: 99%

“…2010; Yokoyama 2011; Koay & Chitre 2013; Chudej et al. 2015; Mahmoudzadeh, Powers & Yazdani 2016). The global approach to path optimization through turbulence is challenging because it requires rapidly updated flow-field measurements or real-time modelling and prediction of the flow.…”

Section: Introductionmentioning

confidence: 99%