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Simon Schopferer

Publications and source records attributed to Simon Schopferer.

2 recordsLinked to original sources

Extensions to the Dubins Airplane Method - Flight Performance Constraints in Uniform Wind

This paper extends the Dubins Airplane Method to generate trajectories for fixed-wing aircraft that remain within flight performance limits in the presence of uniform wind. Within the field of runtime-efficient trajectory generation for fixed-wing aircraft, the Dubins Airplane Method represents a well-received and widely adopted compromise between algorithmic complexity, runtime efficiency, and solution quality. However, its simplistic kinematic model restricts its ability to generate safe and feasible trajectories, especially in the presence of wind. As a mathematical foundation to overcome these limitations, a set of closed-form equations are derived that relate flight performance constraints to kinematic constraints. Building upon existing methods that use trochoids for planning of 2D trajectories in uniform wind, this paper presents a set of iterative algorithms for planning versatile 3D trajectories composed of trochoidal or circular turns and straight flight segments, with constant vertical speed, constant thrust or smooth longitudinal transitions. The presented methods comprise a flexible and modular framework for runtime-efficient generation of performance-aware trajectories based on parametric curves without the need for numerical trajectory representations.

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Drones4Good: Supporting Disaster Relief Through Remote Sensing and AI

In order to respond effectively in the aftermath of a disaster, emergency services and relief organizations rely on timely and accurate information about the affected areas. Remote sensing has the potential to significantly reduce the time and effort required to collect such information by enabling a rapid survey of large areas. To achieve this, the main challenge is the automatic extraction of relevant information from remotely sensed data. In this work, we show how the combination of drone-based data with deep learning methods enables automated and large-scale situation assessment. In addition, we demonstrate the integration of onboard image processing techniques for the deployment of autonomous drone-based aid delivery. The results show the feasibility of a rapid and large-scale image analysis in the field, and that onboard image processing can increase the safety of drone-based aid deliveries.

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