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Larry A. Meyn - One of the best experts on this subject based on the ideXlab platform.

  • probabilistic safety assessment of unmanned aerial system operations
    Journal of Guidance Control and Dynamics, 2013
    Co-Authors: Larry A. Meyn
    Abstract:

    U NMANNED aircraft systems (UASs) are becoming increasingly popular, encompassing awide variety ofmissions ranging from military reconnaissance to wildfire monitoring. However, there are inherent safety concerns with UAS due to the lack of an onboard human pilot. Currently, to operate UAS in the National Airspace System, the operators must obtain either a Certificate of Authorization or Waiver or a Special Airworthiness Certificate from the Federal Aviation Administration (FAA) [1]. One of the important steps in obtaining the approval is a proof that the UAS operation can be conducted at an acceptable level of safety [1]. Many past studies assessing the safety of UAS operations used uniform traffic densities. Anno [2] investigated midair collision risk using a random collision theory and compared the results with historic collision data from 1969 to 1978. McGeer et al. [3,4] performed hazard estimation studies of the Aerosonde UAS. In these studies, two different constant densities were used for the UAS and the background traffic. A comprehensive system-wide study performed byWeibel and Hansman [5] used a ratio of the volume swept by the background aircraft to the total airspace volume. Lum and Waggoner [6] conducted a study on both midair collision and ground impact based on the collision model of gas molecules. These approaches are adequate for obtaining a general idea of the risk around a given region but do not consider traffic patterns that are specific to the region of interest. Lum et al. [7] used actual UAS trajectories for a ground impact analysis. In this study, a realistic distribution of average glide angle was used to calculate the expected value of ground fatalities. Sheridan [8] proposed a model to estimate the relative collision probability between two aircraft at the closest point of approach based on Gaussian density functions. Maki et al. [9] created a method to efficiently estimate the probability of near midair collision using Gaussian probability distributions of proposed UAS trajectories and historic track data. In thiswork, the nearmidair collision probabilities are expressed as confidence intervals. Some of the work is related to quantitatively establishing the boundary of “well clear” for sense-and-avoid systems. Weibel et al. [10] used conditional probability to develop a separation standard model based on uncorrelated encounter model [11]. Asmat et al. [12] developed a UAS-specific collision-avoidance system that can communicate with the existing traffic alert collision and avoidance system. In this work, a distributed traffic model similar to Maki et al. [9] is constructed using actual traffic data collected over a one-year period to enable a probabilistic approach to risk assessment. The radar data provided by the U.S. Air Force contains not only the cooperative traffic data but also the noncooperative traffic data with altitude information. Inclusion of noncooperative traffic, mostly general aviation (GA) traffic, is important because they tend to fly at lower altitudes where the UAS are likely to operate, and it is harder to implement collision mitigation measures with them. The current study computes the collision rates, which are defined by the number of collisions per unit time of UAS operation, based on UAS tracks flying through the continuous background traffic model. The procedures and results are explained in detail throughout the following sections. Following the introduction, the area around the Grand Forks Air Force Base where the U.S. Air Force is planning to operate UAS is described in Sec. II. Then, the description of the continuous-traffic model is presented in Sec. III. In Sec. IV, mathematical formulations for the continuous-traffic model and for the computation of conflict and collision probabilities are presented. Section V reviews the air traffic characteristics of the given area in terms of average aircraft counts and their spatial distributions, and Sec. VI presents the collision risk computed for a potential mission scenario. Finally, the results and recommendations are summarized in Sec. VII.

  • Engineering Notes Probabilistic Safety Assessment of Unmanned Aerial System Operations
    2013
    Co-Authors: Hak-tae Lee, Larry A. Meyn, Soyoung Kim
    Abstract:

    NMANNED aircraft systems (UASs) are becoming increas-inglypopular,encompassingawidevarietyofmissionsrangingfrommilitaryreconnaissancetowildfiremonitoring.However,thereareinherentsafety concernswithUASduetothelackof anonboardhuman pilot. Currently, to operate UAS in the National AirspaceSystem, the operators must obtain either a Certificate of Authoriza-tion or Waiver or a Special Airworthiness Certificate from the FederalAviation Administration (FAA) [1]. One of the important steps inobtaining the approval is a proof that the UAS operation can beconducted at an acceptable level of safety [1].Many past studies assessing the safety of UAS operations useduniform traffic densities. Anno [2] investigated midair collision riskusing a random collision theory and compared the results withhistoric collision data from 1969 to 1978. McGeer et al. [3,4]performedhazardestimationstudiesoftheAerosondeUAS.Inthesestudies, two different constant densities were used for the UAS andthe background traffic. A comprehensive system-wide study per-formedbyWeibelandHansman[5]usedaratioofthevolumesweptby the background aircraft to the total airspace volume. Lum andWaggoner[6]conductedastudyonbothmidaircollisionandgroundimpact based on the collision model of gas molecules. Theseapproaches are adequate for obtaining a general idea of the riskaround a given region but do not consider traffic patterns that arespecific to the region of interest.Lum et al. [7] used actual UAS trajectories for a ground impactanalysis. In this study, a realistic distribution of average glide anglewasusedtocalculatetheexpectedvalueofgroundfatalities.Sheridan[8] proposed a model to estimate the relative collision probabilitybetween two aircraft at the closest point of approach based onGaussian density functions. Maki et al. [9] created a method toefficiently estimate the probability of near midair collision usingGaussian probability distributions of proposed UAS trajectories andhistorictrackdata.Inthiswork,thenearmidaircollisionprobabilitiesare expressed as confidence intervals.Some of the work is related to quantitatively establishing theboundary of “well clear” for sense-and-avoid systems. Weibel et al.[10] used conditional probability to develop a separation standardmodelbasedonuncorrelatedencountermodel[11].Asmatetal.[12]developed a UAS-specific collision-avoidance system that cancommunicate with the existing traffic alert collision and avoidancesystem.Inthiswork,adistributedtrafficmodelsimilartoMakietal.[9]isconstructedusingactualtrafficdatacollectedoveraone-yearperiodto enable a probabilistic approach to risk assessment. The radar dataprovided by the U.S. Air Force contains not only the cooperativetraffic data but also the noncooperative traffic data with altitudeinformation. Inclusion of noncooperative traffic, mostly generalaviation (GA) traffic, is important because they tend to fly at loweraltitudes where the UAS are likely to operate, and it is harder toimplement collision mitigation measures with them. The currentstudy computes the collision rates, which are defined by the numberof collisions per unit time of UAS operation, based on UAS tracksflying through the continuous background traffic model. Theprocedures and results are explained in detail throughout thefollowing sections.Following the introduction, the area around the Grand Forks AirForce Base where the U.S. Air Force is planning to operate UAS isdescribed in Sec. II. Then, the description of the continuous-trafficmodelispresentedinSec.III.InSec.IV,mathematicalformulationsfor the continuous-traffic model and for the computation of conflictand collision probabilities are presented. Section V reviews the airtraffic characteristics of the given area in terms of average aircraftcounts and their spatial distributions, and Sec. VI presents thecollision risk computed for a potential mission scenario. Finally, theresults and recommendations are summarized in Sec. VII.

Soyoung Kim - One of the best experts on this subject based on the ideXlab platform.

  • Engineering Notes Probabilistic Safety Assessment of Unmanned Aerial System Operations
    2013
    Co-Authors: Hak-tae Lee, Larry A. Meyn, Soyoung Kim
    Abstract:

    NMANNED aircraft systems (UASs) are becoming increas-inglypopular,encompassingawidevarietyofmissionsrangingfrommilitaryreconnaissancetowildfiremonitoring.However,thereareinherentsafety concernswithUASduetothelackof anonboardhuman pilot. Currently, to operate UAS in the National AirspaceSystem, the operators must obtain either a Certificate of Authoriza-tion or Waiver or a Special Airworthiness Certificate from the FederalAviation Administration (FAA) [1]. One of the important steps inobtaining the approval is a proof that the UAS operation can beconducted at an acceptable level of safety [1].Many past studies assessing the safety of UAS operations useduniform traffic densities. Anno [2] investigated midair collision riskusing a random collision theory and compared the results withhistoric collision data from 1969 to 1978. McGeer et al. [3,4]performedhazardestimationstudiesoftheAerosondeUAS.Inthesestudies, two different constant densities were used for the UAS andthe background traffic. A comprehensive system-wide study per-formedbyWeibelandHansman[5]usedaratioofthevolumesweptby the background aircraft to the total airspace volume. Lum andWaggoner[6]conductedastudyonbothmidaircollisionandgroundimpact based on the collision model of gas molecules. Theseapproaches are adequate for obtaining a general idea of the riskaround a given region but do not consider traffic patterns that arespecific to the region of interest.Lum et al. [7] used actual UAS trajectories for a ground impactanalysis. In this study, a realistic distribution of average glide anglewasusedtocalculatetheexpectedvalueofgroundfatalities.Sheridan[8] proposed a model to estimate the relative collision probabilitybetween two aircraft at the closest point of approach based onGaussian density functions. Maki et al. [9] created a method toefficiently estimate the probability of near midair collision usingGaussian probability distributions of proposed UAS trajectories andhistorictrackdata.Inthiswork,thenearmidaircollisionprobabilitiesare expressed as confidence intervals.Some of the work is related to quantitatively establishing theboundary of “well clear” for sense-and-avoid systems. Weibel et al.[10] used conditional probability to develop a separation standardmodelbasedonuncorrelatedencountermodel[11].Asmatetal.[12]developed a UAS-specific collision-avoidance system that cancommunicate with the existing traffic alert collision and avoidancesystem.Inthiswork,adistributedtrafficmodelsimilartoMakietal.[9]isconstructedusingactualtrafficdatacollectedoveraone-yearperiodto enable a probabilistic approach to risk assessment. The radar dataprovided by the U.S. Air Force contains not only the cooperativetraffic data but also the noncooperative traffic data with altitudeinformation. Inclusion of noncooperative traffic, mostly generalaviation (GA) traffic, is important because they tend to fly at loweraltitudes where the UAS are likely to operate, and it is harder toimplement collision mitigation measures with them. The currentstudy computes the collision rates, which are defined by the numberof collisions per unit time of UAS operation, based on UAS tracksflying through the continuous background traffic model. Theprocedures and results are explained in detail throughout thefollowing sections.Following the introduction, the area around the Grand Forks AirForce Base where the U.S. Air Force is planning to operate UAS isdescribed in Sec. II. Then, the description of the continuous-trafficmodelispresentedinSec.III.InSec.IV,mathematicalformulationsfor the continuous-traffic model and for the computation of conflictand collision probabilities are presented. Section V reviews the airtraffic characteristics of the given area in terms of average aircraftcounts and their spatial distributions, and Sec. VI presents thecollision risk computed for a potential mission scenario. Finally, theresults and recommendations are summarized in Sec. VII.

Christopher Chase Poorman - One of the best experts on this subject based on the ideXlab platform.

  • Drone Nation: Policy Considerations for Allowing Commercial Small Unmanned Aircraft Systems Into the National Airspace System
    Texas A&M Journal of Property Law, 2015
    Co-Authors: Christopher Chase Poorman
    Abstract:

    This Comment aims to show that current regulation, or more precisely non-regulation of commercial UAS should be modified, and operators should be allowed to conduct commercial operations without subjecting UAS to the high standards of other “aircraft.” Per Congress’s mandate, the FAA should immediately create and enforce practically sound standards for small-scale, commercial UAS that operate inside the NAS while avoiding unnecessary and costly administrative burdens. Congress should modify the currently voluntary standards, instead of mandating that operators adhere to specific commercial use guidelines without requiring an arduous approval process for commercial flight, such as the current Special Airworthiness Certificate and Section 333 exemption. This Comment will not address the issues facing larger, interstate drones that will operate outside of the visual sight or immediate area of the operator.

Denney Ewen - One of the best experts on this subject based on the ideXlab platform.

  • Argument-Based Airworthiness Assurance of Small UAS
    2015
    Co-Authors: Pai Ganesh, Denney Ewen
    Abstract:

    Presently, there are three avenues by which Unmanned Aircraft System (UAS) operations are authorized in the U.S. National Airspace System (NAS): obtaining either (i) a Certificate of authorization (COA), or (ii) a Special Airworthiness Certificate (SAC) in either the experimental, or the restricted category, or (iii) an exemption from an Airworthiness Certificate together with a civil COA. The first is meant primarily for public entities, such as NASA; the remaining two are the only available means for civil UAS operations. Recently, the Federal Aviation Administration (FAA) has also proposed a regulatory framework targeted for certain small UAS, specifically those weighing 55 pounds or less, although final rulemaking remains pending. We have previously shown how an assurance case can aggregate heterogeneous reasoning and safety evidence, with application to UAS safety. In this paper, we describe how assurance cases can serve as a common framework to justify overall system safety, unifying both operational aspects and Airworthiness, in particular system design assurance. We also show how this approach can coexist with, and augment, existing safety analysis processes and best-practices, by transforming the artifacts they produce into structured assurance arguments. To illustrate the applicability and utility of our approach, we have been applying it for the design assurance of an unmanned rotorcraft system, intended for precision agriculture operations, as part of the NASA Unmanned Aircraft System (UAS) Integration in the National Airspace System (NAS) project

Hak-tae Lee - One of the best experts on this subject based on the ideXlab platform.

  • Engineering Notes Probabilistic Safety Assessment of Unmanned Aerial System Operations
    2013
    Co-Authors: Hak-tae Lee, Larry A. Meyn, Soyoung Kim
    Abstract:

    NMANNED aircraft systems (UASs) are becoming increas-inglypopular,encompassingawidevarietyofmissionsrangingfrommilitaryreconnaissancetowildfiremonitoring.However,thereareinherentsafety concernswithUASduetothelackof anonboardhuman pilot. Currently, to operate UAS in the National AirspaceSystem, the operators must obtain either a Certificate of Authoriza-tion or Waiver or a Special Airworthiness Certificate from the FederalAviation Administration (FAA) [1]. One of the important steps inobtaining the approval is a proof that the UAS operation can beconducted at an acceptable level of safety [1].Many past studies assessing the safety of UAS operations useduniform traffic densities. Anno [2] investigated midair collision riskusing a random collision theory and compared the results withhistoric collision data from 1969 to 1978. McGeer et al. [3,4]performedhazardestimationstudiesoftheAerosondeUAS.Inthesestudies, two different constant densities were used for the UAS andthe background traffic. A comprehensive system-wide study per-formedbyWeibelandHansman[5]usedaratioofthevolumesweptby the background aircraft to the total airspace volume. Lum andWaggoner[6]conductedastudyonbothmidaircollisionandgroundimpact based on the collision model of gas molecules. Theseapproaches are adequate for obtaining a general idea of the riskaround a given region but do not consider traffic patterns that arespecific to the region of interest.Lum et al. [7] used actual UAS trajectories for a ground impactanalysis. In this study, a realistic distribution of average glide anglewasusedtocalculatetheexpectedvalueofgroundfatalities.Sheridan[8] proposed a model to estimate the relative collision probabilitybetween two aircraft at the closest point of approach based onGaussian density functions. Maki et al. [9] created a method toefficiently estimate the probability of near midair collision usingGaussian probability distributions of proposed UAS trajectories andhistorictrackdata.Inthiswork,thenearmidaircollisionprobabilitiesare expressed as confidence intervals.Some of the work is related to quantitatively establishing theboundary of “well clear” for sense-and-avoid systems. Weibel et al.[10] used conditional probability to develop a separation standardmodelbasedonuncorrelatedencountermodel[11].Asmatetal.[12]developed a UAS-specific collision-avoidance system that cancommunicate with the existing traffic alert collision and avoidancesystem.Inthiswork,adistributedtrafficmodelsimilartoMakietal.[9]isconstructedusingactualtrafficdatacollectedoveraone-yearperiodto enable a probabilistic approach to risk assessment. The radar dataprovided by the U.S. Air Force contains not only the cooperativetraffic data but also the noncooperative traffic data with altitudeinformation. Inclusion of noncooperative traffic, mostly generalaviation (GA) traffic, is important because they tend to fly at loweraltitudes where the UAS are likely to operate, and it is harder toimplement collision mitigation measures with them. The currentstudy computes the collision rates, which are defined by the numberof collisions per unit time of UAS operation, based on UAS tracksflying through the continuous background traffic model. Theprocedures and results are explained in detail throughout thefollowing sections.Following the introduction, the area around the Grand Forks AirForce Base where the U.S. Air Force is planning to operate UAS isdescribed in Sec. II. Then, the description of the continuous-trafficmodelispresentedinSec.III.InSec.IV,mathematicalformulationsfor the continuous-traffic model and for the computation of conflictand collision probabilities are presented. Section V reviews the airtraffic characteristics of the given area in terms of average aircraftcounts and their spatial distributions, and Sec. VI presents thecollision risk computed for a potential mission scenario. Finally, theresults and recommendations are summarized in Sec. VII.