Emergency Trajectory Structure for UAVs

The study of the design of emergency trajectories of air vehicles is one of the key elements in improving airspace safety for air vehicles. The aim is to lighten pilots’ workload, offering quick and effective solutions. However, almost all flight optimizers proposed in the literature still need to be completed when it comes to resolving emergency contexts, which presents a significant disadvantage to the advancement of scientific research. This resolution is based on the following problems: (a) finding paths free of obstacles, (b) ensuring their flight capacity, and finally, (c) calculating trajectories optimizing several criteria with a calculation time constraint (a few minutes). This document analyzes the safety landing problem and proposes an architecture that effectively reduces complexity and ensures solvability within a reasonable computational time. This architectural framework is designed to be adaptable, allowing for testing several algorithms to provide a quick overview of their strengths and weaknesses in this context. The primary aim of these tests is to benchmark the computational time of the overall architecture, ensuring that this adaptable framework is fully capable of handling the problem’s complexity. It is important to note that the algorithms chosen address only a simplified version of the problem. The initial results are promising in terms of time response and the potential to enhance the representativeness and complexity of the problem. The next phase of our research will focus on striking the right balance between complexity, representativity, and computational time, aiming to impact emergency response significantly.

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Publication Details

Journal
Aerospace
Published
2024-12-31
DOI
https://doi.org/10.3390/aerospace12010021
Primary Topic
Robotic Path Planning Algorithms
Type
article
Field-Weighted Citation Impact
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article

Emergency Trajectory Structure for UAVs

Pierre-Loïc Garoche, Damien Goubinat, Daniel Delahaye, Maëva Ongale-Obeyi
1 links
Aerospace
Robotic Path Planning Algorithms
article

Emergency Trajectory Structure for UAVs

Pierre-Loïc Garoche, Damien Goubinat, Daniel Delahaye, Maëva Ongale-Obeyi
article en

Abstract

The study of the design of emergency trajectories of air vehicles is one of the key elements in improving airspace safety for air vehicles. The aim is to lighten pilots’ workload, offering quick and effective solutions. However, almost all flight optimizers proposed in the literature still need to be completed when it comes to resolving emergency contexts, which presents a significant disadvantage to the advancement of scientific research. This resolution is based on the following problems: (a) finding paths free of obstacles, (b) ensuring their flight capacity, and finally, (c) calculating trajectories optimizing several criteria with a calculation time constraint (a few minutes). This document analyzes the safety landing problem and proposes an architecture that effectively reduces complexity and ensures solvability within a reasonable computational time. This architectural framework is designed to be adaptable, allowing for testing several algorithms to provide a quick overview of their strengths and weaknesses in this context. The primary aim of these tests is to benchmark the computational time of the overall architecture, ensuring that this adaptable framework is fully capable of handling the problem’s complexity. It is important to note that the algorithms chosen address only a simplified version of the problem. The initial results are promising in terms of time response and the potential to enhance the representativeness and complexity of the problem. The next phase of our research will focus on striking the right balance between complexity, representativity, and computational time, aiming to impact emergency response significantly.

AerospaceVol. 12(1)
École Nationale de l’Aviation Civile (FR), Université Fédérale de Toulouse Midi-Pyrénées (FR), Thales (Canada) (CA), Thales (France) (FR)
Openalex Percentile: Top 75%
Robotic Path Planning Algorithms
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