A Model of Human Harm from a Falling Unmanned Aircraft: Implications for UAS Regulation

Shelley, Andrew

doi:10.15394/ijaaa.2016.1120

Cited by 24 publications

(12 citation statements)

References 17 publications

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“…Census data are often used to estimate ρ G r ðÞ [10,14,16,23]. With respect to the lethal area, two crash modes are often considered in the literature: vertical free fall [10,22,23] and unpremeditated, gliding descent [10,11,13,16]. For simplicity, this work assumes that the ground impact occurs following a vertical free fall so that the impact location is close to the point where the initiating failure has occurred.…”

Section: Strike 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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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: Strike 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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“…The literature contains several studies of the societal impact of drones [10], drone use in the context of governance, ethics, and privacy [11], growth of the drone market [12], possibilities of using drones in urban environments [13], drone efficiency [2], the economic benefits for last-cm delivery with trucks [10], and the dangers of falling drones [15]. However, no field studies have yet investigated practicalaspects of drone delivery such as the behaviour of inexperienced users when operating delivery drones, the reliability of the hardware and software, and the impact of weather conditions on day-to-day operations.…”

mentioning

confidence: 99%

Last-Centimeter Personal Drone Delivery: Field Deployment and User Interaction

Kornatowski

Bhaskaran

Heitz

et al. 2018

IEEE Robot. Autom. Lett.

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Drones are rapidly becoming an affordable and often faster solution for parcel delivery than terrestrial vehicles. Existing transportation drones and software infrastructures are mostly designed by logistics companies for trained users and dedicated infrastructure, and are to be used for either long range (<150 km) or last-mile delivery (<20 km). This letter presents Dronistics, an integrated software and hardware system for last-centimeter (<5 km) person-to-person delivery using cargo drones. The system is conceived to be intuitive and intrinsically safe to enable short-distance deliveries between inexperienced users. Dronistics is composed of a safe foldable drone (PackDrone) and a web application software to intuitively control and track the drone in real time. In order to assess Dronistics' user acceptance, we conducted 150 deliveries over one month on the EPFL campus in Switzerland. Here we describe the results of these tests by analyzing flight statistics, environmental conditions, and user reactions. Moreover, we also describe technical problems that occurred during flight tests and solutions that could prevent them. Index Terms-Aerial systems, applications, intelligent transportation systems, unmanned aerial vehicles. I. INTRODUCTION R ECENT years have witnessed an exponential rise in interest in delivery drones, and mainly multicopters, due to their capability to effectively overcome obstacles or traffic jams, to rapidly reach remote locations, and to take off and land in cluttered environments. Therefore, logistics companies have started to explore the possibility of using aerial delivery as a faster and more cost-effective alternative to terrestrial transportation [1], [2]. Examples include Amazon.com's tests of product deliveries to homes directly from warehouses in the United Kingdom [3], DHL's deliveries of emergency medical supplies using its Parcelcopter [4], Alphabet's burrito deliveries to Australian homes with its Project Wing drones [5], Swiss Post's experiments with transportation of lab samples between hospitals in Lugano, Switzerland [6], and Zipline's transportation of blood from central storehouses to remote hospitals in Africa [7]. All these aerial delivery services are developed for operation by trained employees of logistics companies for

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“…The strike model represents the conditional probability that an impact at a specific location strikes a person. In the literature, this factor is commonly modeled as follows [10,22,27,73,110,151]:…”

Section: Strike Modelmentioning

confidence: 99%

“…The vertical free fall mode is usually associated with loss of control conditions where the aircraft crashes at high velocity. The lethal area for this mode is commonly modeled as follows [27,109,151]:…”

Section: Strike 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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A Model of Human Harm from a Falling Unmanned Aircraft: Implications for UAS Regulation

Cited by 24 publications

References 17 publications

Trajectory-Based, Probabilistic Risk Model for UAS Operations

Trajectory-Based, Probabilistic Risk Model for UAS Operations

Last-Centimeter Personal Drone Delivery: Field Deployment and User Interaction

Automated Contingency Management in Unmanned Aircraft Systems

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