Accurate Ground Impact Footprints and Probabilistic Maps for Risk Analysis of UAV Missions

Levasseur, Baptiste; Bertrand, Sylvain; Raballand, N.; Viguier, F.; Goussu, Gregoire

doi:10.1109/aero.2019.8741718

Cited by 16 publications

(23 citation statements)

References 12 publications

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“…To fully understand the parametric considerations of the ground impact probability maps and the dynamic assumptions made on the descent model, it is worth briefly recalling the map generation pipeline previously developed by the authors in [9]. The failure case considered is a total loss of control due to a power failure.…”

Section: A Stochastic Generation Of Impact Pointsmentioning

confidence: 99%

“…From these parameters, we then obtain the associated state vector χ 0 and control vector u 0 . Real flight trajectories of an UAV performing cruise-like flight mode in straight line and constant altitude with respect to the ground were used to estimate the dispersion of the parameters R 0 , γ 0 (see [9]). The distribution of this data is approximated by a Gaussian distribution and is used to sample the values R 0 , γ 0 .…”

Section: A Stochastic Generation Of Impact Pointsmentioning

confidence: 99%

“…This assumption neglects the contribution from a dynamic point of view of external factors having a strong influence on lift, such as wind, for example. In previous work done by the authors [9] a model of uncontrolled descent to ground has been developed by considering a 6 degrees of freedom (6DoF) dynamic model of a fixedwing UAV including the effect of wind in flight mechanics. From this precise model, a first work was to consider the footprints corresponding to the extreme zones accessible by the UAV in case of glide descent.…”

Section: Introductionmentioning

confidence: 99%

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Efficient Generation of Ground Impact Probability Maps by Neural Networks for Risk Analysis of UAV Missions

Levasseur

Bertrand

Raballand

2020

2020 International Conference on Unmanned Aircraft Systems (ICUAS)

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This paper investigates the generation of ground impact probability maps for UAVs in case of failure during the flight. Such maps are of a huge interest for risk assessment of UAV operations and can be used both for offline mission preparation or analysis and online decision making. Two approaches are proposed in this paper to generate such maps, taking into account a dynamical model a fixed-wing UAV and wind conditions. The first one relies on the generation of a complete database by Monte Carlo simulations. The second one is based on neural network surrogate models obtained by supervised learning using this database. Computation time required by the second approach is very small and compatible with online use. The two approaches are presented and discussed, and examples of ground impact probability maps generated are provided.

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Section: A Stochastic Generation Of Impact Pointsmentioning

confidence: 99%

Section: A Stochastic Generation Of Impact Pointsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Efficient Generation of Ground Impact Probability Maps by Neural Networks for Risk Analysis of UAV Missions

Levasseur

Bertrand

Raballand

2020

2020 International Conference on Unmanned Aircraft Systems (ICUAS)

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“…This area is defined by assuming a gliding descent to the ground and is therefore parametrized by the gliding range of the vehicle. Note that improved models can be used instead for this definition, such as the ones developed in [11]. The third term, Pr{collision|impact∩loss}, takes into account the surface of collision between the UAV and someone as well as the population density at ground.…”

Section: Pr{casualty}=pr{loss}mentioning

confidence: 99%

Feasibility Analysis of UAV Operations for Monitoring of Infrastructure Networks: a Risk-based Approach

Bertrand

Raballand

Lala

et al. 2019

2019 International Conference on Unmanned Aircraft Systems (ICUAS)

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This paper proposes a method to analyze the feasibility of monitoring missions of infrastructure networks (eg. railways, roads, power lines, etc.) by Unmanned Aerial Vehicles (UAVs). The method consists in evaluating the risk at ground for people due to an UAV flying over each part of the network. Based on a risk categorization defined by probability and exposition time, several metrics are developed to analyze the feasibility of flights in terms of risk. More specifically, these metrics are related to the cumulative length of feasible flight segments and their connectedness. A decision making process is then defined to automatically classify all the lines of the network in a comprehensive and synthetic way. The whole method takes into account flight regulation and flight operation constraints as well as considerations relative to business interests in asset management of the infrastructure network. An illustration is proposed through a benchmark example on the French railway network.

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“…UAVs in urban areas may fall and hit people and vehicles on the ground [6][7]. UAVs may also intrude into airport airspaces [8].…”

Section: Introductionmentioning

confidence: 99%

Risk Assessment Model for UAV Cost-Effective Path Planning in Urban Environments

Pang

Dai

et al. 2020

IEEE Access

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Increasing use of Unmanned Aerial Vehicle (UAV) in urban environments poses to an increased risk of fallen UAVs impacting people and vehicles on the ground, as well as colliding with manned aircraft in the vicinity of airports. Risk management of UAV flights for safe operations is essential. We proposed a comprehensive risk assessment model for UAV operation in urban environments. Three risk categories (people, vehicles, and manned aircraft) were considered and each risk cost was quantified using collision probability. We adjusted the risk costs in various magnitudes to a same scale and conducted a sensitivity analysis to determine the optimal coefficients of the three risk cost models. We then computed the total risk and generated a risk cost map for path planning. Modified path planning algorithms were used to produce a cost-effective path, and we compared their performances in terms of total risk cost and computational time. Lastly, we performed simulations to validate the feasibility and effectiveness of our proposed risk assessment model. The results show that the risk-cost-based path planning method can generate safer path for UAV operations than the traditional shortest-distance-based method. Our proposed model can be extended to complex urban environments by including more relevant parameters and data. INDEX TERMS Unmanned aerial vehicle, risk assessment model, risk cost map, path planning, urban environments Fuqing Dai holds the Dean's Chair in Air Traffic Management School at the Civil Aviation University of China. He is also a professor focusing on the research of airspace planning and management, flight procedures design and optimization, UTM and ATM systems. Professor Dai obtained his master's degree from Ecole Nationale de l'Aviation Civile, France and bachelor's degree from Nanjing University, China.

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Accurate Ground Impact Footprints and Probabilistic Maps for Risk Analysis of UAV Missions

Cited by 16 publications

References 12 publications

Efficient Generation of Ground Impact Probability Maps by Neural Networks for Risk Analysis of UAV Missions

Efficient Generation of Ground Impact Probability Maps by Neural Networks for Risk Analysis of UAV Missions

Feasibility Analysis of UAV Operations for Monitoring of Infrastructure Networks: a Risk-based Approach

Risk Assessment Model for UAV Cost-Effective Path Planning in Urban Environments

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