paper-with-me

홈 › Papers

Certifying Emergency Landing for Safe Urban UAV

2021-04-30 · Joris Guerin, Kevin Delmas, Jérémie Guiochet

Unmanned Aerial Vehicles (UAVs) have the potential to be used for many applications in urban environments. However, allowing UAVs to fly above densely populated areas raises concerns regarding safety. One of the main safety issues is the possibility for a failure to cause the loss of navigation capabilities, which can result in the UAV falling/landing in hazardous areas such as busy roads, where it can cause fatal accidents. Current standards, such as the SORA published in 2019, do not consider applicable mitigation techniques to handle this kind of hazardous situations. Consequently, certifying UAV urban operations implies to demonstrate very high levels of integrity, which results in prohibitive development costs. To address this issue, this paper explores the concept of Emergency Landing (EL). A safety analysis is conducted on an urban UAV case study, and requirements are proposed to enable the integration of EL as an acceptable mitigation mean in the SORA. Based on these requirements, an EL implementation was developed, together with a runtime monitoring architecture to enhance confidence in the system. Preliminary qualitative results are presented and the monitor seem to be able to detect errors of the EL system effectively.

📄 PDF Abstract BibTeX arXiv:2104.14928

Code (0)

등록된 구현이 없습니다.

Similar Papers 제목 키워드 기반

Vision-Based Risk Aware Emergency Landing for UAVs in Complex Urban Environments

2025-05-26 · Julio de la Torre-Vanegas, Miguel Soriano-Garcia, Israel Becerra, Diego Mercado-Ravell

Landing safely in crowded urban environments remains an essential yet challenging endeavor for Unmanned Aerial Vehicles (UAVs), especially in emergency situations. In this work, we propose a risk-aware approach that harn…

Semantic Segmentation

Risk Assessment for Autonomous Landing in Urban Environments using Semantic Segmentation

2024-10-16 · Jesús Alejandro Loera-Ponce, Diego A. Mercado-Ravell, Israel Becerra-Durán, Luis Manuel Valentin-Coronado

In this paper, we address the vision-based autonomous landing problem in complex urban environments using deep neural networks for semantic segmentation and risk assessment. We propose employing the SegFormer, a state-of…

SegmentationSemantic Segmentation

Visual-based Safe Landing for UAVs in Populated Areas: Real-time Validation in Virtual Environments

2022-03-25 · Hector Tovanche-Picon, Javier Gonzalez-Trejo, Angel Flores-Abad, Diego Mercado-Ravell

Safe autonomous landing for Unmanned Aerial Vehicles (UAVs) in populated areas is a crucial aspect for successful urban deployment, particularly in emergency landing situations. Nonetheless, validating autonomous landing…

Lander.AI: Adaptive Landing Behavior Agent for Expertise in 3D Dynamic Platform Landings

2024-03-11 · Robinroy Peter, Lavanya Ratnabala, Demetros Aschu, Aleksey Fedoseev 외

Mastering autonomous drone landing on dynamic platforms presents formidable challenges due to unpredictable velocities and external disturbances caused by the wind, ground effect, turbines or propellers of the docking pl…

Deep Reinforcement LearningNavigate

Evaluation of Runtime Monitoring for UAV Emergency Landing

2022-02-07 · Joris Guerin, Kevin Delmas, Jérémie Guiochet

To certify UAV operations in populated areas, risk mitigation strategies -- such as Emergency Landing (EL) -- must be in place to account for potential failures. EL aims at reducing ground risk by finding safe landing ar…