Preliminary Risk Assessment for Small Unmanned Aircraft Systems

Barr, Lawrence C; Newman, Richard L.; Ancel, Ersin; Belcastro, Christine M.; Foster, John V.; Evans, Joni K.; Klyde, David H.

doi:10.2514/6.2017-3272

Cited by 66 publications

(50 citation statements)

References 10 publications

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“…By contrast, this work suggests modeling λ impact using Bayesian Belief Networks (BBNs), which provides two major advantages: 1. The model can be supplied with both qualitative and quantitative data simultaneously [15]. This is specially useful in models with high degree of uncertainty, like in the problem under study.…”

Section: Impact Modelmentioning

confidence: 99%

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Trajectory-Based, Probabilistic Risk Model for UAS Operations

Usach¹,

Vila²,

Gallego³

2020

Risk Assessment in Air Traffic Management

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To enable the safe integration of Unmanned Aircraft System (UAS) into the civil airspace, the European Aviation Safety Agency (EASA) has elaborated a new regulatory framework that is operation-centric and risk-based. Based on this principle, gaining authorization to conduct certain types of operations depends on a safety risk assessment. To harmonize this process, the Joint Authorities for Rulemaking on Unmanned Systems (JARUS) released a qualitative methodology called Specific Operation Risk Assessment (SORA). However, SORA is not a complete safety assessment tool since, in some cases, a quantitative risk analysis is still required. This work develops a probabilistic risk model that extends SORA to evaluate the ground risk and the air risk components along a specified UAS trajectory quantitatively. The proposed model is supplied with illustrative data and is validated in a representative UAS mission. In the future, the risk model will be exploited to develop a decision tool for determining the minimum-risk trajectory when multiple, alternative routes are available.

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Section: Impact Modelmentioning

confidence: 99%

“…In order to derive λ impact from P impact , it is necessary to assume a given probability distribution function. As in similar approaches in the literature (e.g., see [15,21]), this work assumes that P impact follows a Poisson distribution, so λ impact is given by:…”

Section: Impact Modelmentioning

confidence: 99%

Trajectory-Based, Probabilistic Risk Model for UAS Operations

Usach¹,

Vila²,

Gallego³

2020

Risk Assessment in Air Traffic Management

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“…Gonçalves et al [19] presented a safety assessment process model for a UAV using Petri Nets, while [20] used Bayesian Belief Networks for performing risk analysis of small unmanned aircraft systems.…”

Section: A Problem Statement and Motivationmentioning

confidence: 99%

Qualitative and Quantitative Risk Analysis and Safety Assessment of Unmanned Aerial Vehicles Missions Over the Internet

et al. 2019

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In the last few years, Unmanned Aerial Vehicles (UAVs) are making a revolution as an emerging technology with many different applications in the military, civilian, and commercial fields. The advent of autonomous drones has initiated serious challenges, including how to maintain their safe operation during their missions. The safe operation of UAVs remains an open and sensitive issue, since any unexpected behavior of the drone or any hazard would lead to potential risks that might be very severe. The motivation behind this work is to propose a methodology for the safety assurance of drones over the Internet (Internet of drones (IoD)). Two approaches will be used in performing the safety analysis: (1) a qualitative safety analysis approach, and (2) a quantitative safety analysis approach. The first approach uses the international safety standards, namely ISO 12100 and ISO 13849 to assess the safety of drone's missions by focusing on qualitative assessment techniques. The methodology starts from hazard identification, risk assessment, risk mitigation, and finally draws the safety recommendations associated with a drone delivery use case. The second approach presents a method for the quantitative safety assessment using Bayesian Networks (BN) for probabilistic modeling. BN utilizes the information provided by the first approach to model the safety risks related to UAVs' flights. An illustrative UAV crash scenario is presented as a case study, followed by a scenario analysis, to demonstrate the applicability of the proposed approach. These two analyses, qualitative and quantitative, enable all involved stakeholders to detect, explore and address the risks of UAV flights, which will help the industry to better manage the safety concerns of UAVs.

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“…Contingencies are unforeseen events with the potential of causing an emergency. As an example, the works in [16,17,75,172] identified several important contingencies in UAS operations. Some of them are common to manned aviation -like the loss of separation with a transient aircraft or the loss of control-, while others are specific to UAS.…”

Section: Contingency Management Approachmentioning

confidence: 99%

“…Possible modeling approaches include Fault Tree Analysis (FTA) and Event Tree Analysis (ETA), among others. In recent years, Bayesian Belief Networks (BBNs) are also adopted to this effect [10,16,168], providing a number of advantages.…”

Section: Impact Modelmentioning

confidence: 99%

Automated Contingency Management in Unmanned Aircraft Systems

Molina¹

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Technological development and scientific research are steadily enabling higher levels of automation in the global industry. In the aerospace sector, the operation of Unmanned Aircraft System (UAS) is a clear example. Given the huge potential of the UAS market, Civil Aviation Authorities are elaborating a new regulatory framework for the safe integration of UAS into the civil airspace. The general goal is ensuring that the operation of UAS has an Equivalent Level of Safety (ELOS) to that of manned aviation. To meet the previous goal, this thesis advocates for increasing the level of automation of UAS operations by providing the automatic system on-board the aircraft with Automated Contingency Management (ACM) functions. ACM functions are designed to assist the pilot-in-command in case a contingency, and ultimately to fully replace the pilot if this is required by the situation (e.g. due to a Command and Control (C2) link loss) or if the pilot decides so. However, in order for automation to be safe, automated functions must be developed following safe design methodologies based on aerospace standards. Agraïments Vull agrair la col•laboració de totes aquelles persones i institucions que han fet possible la realització d'aquesta tesi: En primer lloc, vull fer constar que aquesta tesi ha estat co-financiada pel Fons Social Europeu 2014-2020 i pel programa VALI+d de la Generalitat Valenciana (expedient número ACIF/2016/197). De forma molt especial, vull agrair a Joan Vila la seua dedicació al llarg de tots aquests anys. El seu recolçament i la seua tasca de direcció han estat fonamentals per dur a terme aquest treball. També vull agrair a tots els membres del Department of Unmanned Aircraft del German Aerospace Center-Institute of Flight Systems la seua acollida durant la meua estada a Braunschweig; i en especial a Jörg Dittrich, director del departament; Florian Adolf, supervisor de la estada; i Christoph Torens, per la seua inestimable ajuda. Finalment, vull agrair a la meua familia el seu suport i afecte (tot i el meu geni durant la recta final!): als meus pares, el millor exemple; a Pau i Ana, pels seus consells i comprensió; a Marc, el més bonic; i a Irene, que val el cel.

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Preliminary Risk Assessment for Small Unmanned Aircraft Systems

Cited by 66 publications

References 10 publications

Trajectory-Based, Probabilistic Risk Model for UAS Operations

Trajectory-Based, Probabilistic Risk Model for UAS Operations

Qualitative and Quantitative Risk Analysis and Safety Assessment of Unmanned Aerial Vehicles Missions Over the Internet

Automated Contingency Management in Unmanned Aircraft Systems

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