Modeling Unmanned Aerial System (UAS) Risks via Monte Carlo Simulation

Rudnick-Cohen, Eliot; Herrmann, Jeffrey W.; Azarm, Shapour

doi:10.1109/icuas.2019.8798313

Cited by 5 publications

(6 citation statements)

References 16 publications

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“…As follow on research, the aim is to use the validated MBS model simulation as a replacement of the RCC and BC models that have so far been used in other works [1][2][3][4][5][6][7] on assessing third party risk that is posed by UAS operations to persons on the ground. As has been explained in the introduction, this asks for integration of MBS model simulation with four other models, i.e.…”

Section: Discussionmentioning

confidence: 99%

“…One of the major challenges of allowing unmanned aircraft system (UAS) operations in rural and urban areas is to predict and subsequently mitigate safety risk posed to third parties on the ground. Models of safety risk posed to third parties on the ground consist of five probabilistic models [1][2][3][4][5][6][7]. The first model is for the frequency of a UAS ground crash.…”

Section: Introductionmentioning

confidence: 99%

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Modeling and Simulating Human Fatality due to Quadrotor UAS Impact

Rattanagraikanakorn

Blom

Sharpanskykh

et al. 2020

Aiaa Aviation 2020 Forum

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Evaluating safety risk posed to third parties on the ground due to UAS impact requires a model of probability of fatality (PoF) for human. For quadrotor UAS, the existing impact models predict remarkably different PoFs. The most pessimistic is the impact model adopted by Range Commanders Council (RCC) while the Blunt Criterion model is far more optimistic. The ASSURE study has assessed the third set of PoF values through conducting controlled drop tests of a DJI Phantom III on a crash dummy; these results differ again. To investigate these discrepancies, this paper employs a numerical impact analysis of UAS collisions on humans. The current paper is the third in a series of studies. The first study developed a Multi-Body System (MBS) simulation model of a DJI Phantom III impacting the head of a crash dummy; this MBS model has been validated against the experimental drop test results of ASSURE. The second study conducted simulations with the validated MBS model to systematically show the differences in head and neck injuries if the human dummy is replaced by a validated MBS model of a human body. The aim of the current paper is threefold: i) to extend the latter MBS model to assess injury levels for DJI Phantom III impact on thorax and abdomen; ii) to transform the assessed injury levels for head, thorax and abdomen to PoFs; and iii) to compare the MBS obtained PoFs to those from RCC and Blunt Criteria models. The MBS based results show that variations in the scenario of DJI Phantom III impact on the head significantly affect PoF. These variations are not captured by the RCC or BC model, and neither in the ASSURE measurements. Both for head, thorax and abdomen, in case of comparable impact scenarios, the RCC model tends to over-predicts PoF compared to the MBS model, while the BC model tends to under-predict PoF.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Modeling and Simulating Human Fatality due to Quadrotor UAS Impact

Rattanagraikanakorn

Blom

Sharpanskykh

et al. 2020

Aiaa Aviation 2020 Forum

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“…Uncertainties on drag, initial speed at the instant of failure and external wind are accounted for. Full flight dynamics of a Cesna 182 aircraft are used in [10] to compute ground impact probability maps by Monte Carlo simulations. Total loss of power is assumed and uncertainties on the initial conditions of the UAV at failure instant as well as on the deflection of unactuated control surfaces are considered.…”

Section: Introductionmentioning

confidence: 99%

“…Since Monte Carlo simulations can be time-consuming, more recent works have been dedicated to the development of surrogate models for the generation of ground impact probability maps. K-Nearest neighbors models have been considered in [10] to approximate impact probability distribution. Other techniques such as Krigging have been investigated [7] regarding impact footprints or neural networks for both generation of impact footprints [7] and probability maps [11].…”

Section: Introductionmentioning

confidence: 99%

Drone Ground Impact Footprints with Importance Sampling: Estimation and Sensitivity Analysis

2021

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This paper addresses the estimation of accurate extreme ground impact footprints and probabilistic maps due to a total loss of control of fixed-wing unmanned aerial vehicles after a main engine failure. In this paper, we focus on the ground impact footprints that contains 95%, 99% and 99.9% of the drone impacts. These regions are defined here with density minimum volume sets and may be estimated by Monte Carlo methods. As Monte Carlo approaches lead to an underestimation of extreme ground impact footprints, we consider in this article multiple importance sampling to evaluate them. Then, we perform a reliability oriented sensitivity analysis, to estimate the most influential uncertain parameters on the ground impact position. We show the results of these estimations on a realistic drone flight scenario.

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“…Motion planners typically optimize solutions over path distance, time, and obstacle/terrain avoidance with benchmarks as discussed in [13]. Recent papers have presented flight risk metrics that augment traditional distance/time/obstacle avoidance cost terms [14][15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Urban Metric Maps for Small Unmanned Aircraft Systems Motion Planning

Ochoa¹,

Atkins²

2021

Preprint

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Low-altitude urban flight planning for small Unmanned Aircraft Systems (UAS) requires accurate vehicle, environment maps, and risk models to assure flight plans consider the urban landscape as well as airspace constraints. This paper presents a suite of motion planning metrics designed for small UAS urban flight. We define map-based and path-based metrics to holistically characterize motion plan quality. Proposed metrics are examined in the context of representative geometric, graph-based, and sampling-based motion planners applied to a multicopter small UAS. A novel multi-objective heuristic is proposed and applied for graphbased and sampling motion planners at four urban UAS flight altitude layers. Monte Carlo case studies in a New York City urban environment illustrate metric map properties and planner performance. Motion plans are evaluated as a function of planning algorithm, location, range, and flight altitude.

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Modeling Unmanned Aerial System (UAS) Risks via Monte Carlo Simulation

Cited by 5 publications

References 16 publications

Modeling and Simulating Human Fatality due to Quadrotor UAS Impact

Modeling and Simulating Human Fatality due to Quadrotor UAS Impact

Drone Ground Impact Footprints with Importance Sampling: Estimation and Sensitivity Analysis

Urban Metric Maps for Small Unmanned Aircraft Systems Motion Planning

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