Risk Assessment of Flight Paths for Automatic Emergency Parachute Deployment in UAVs

Bleier, M.; Settele, Ferdinand; Krauß, Markus; Knoll, Alexander; Schilling, Klaus

doi:10.1016/j.ifacol.2015.08.080

Cited by 13 publications

(20 citation statements)

References 6 publications

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“…For example, Rackliffe et al [1] combined different information sources in the form of a GIS data to produce a raster (i.e., a cost matrix) that can be used for landing site identification, selection, and flight planning. Such a matrix can be two-dimensional [11] or three-dimensional [3]. The case study presented in [5] relied only on databases to identify candidate landing sites directly from the raw vector data and used some of the properties of road segments identified as landing site candidates for utility evaluation.…”

Section: A Emergency Flight Planningmentioning

confidence: 99%

“…For UAS, risk to humans only occurs when the UAS collides with a manned aircraft or crashes into a populated area. Previous work [10,11] has aimed to minimize risk at a potential emergency landing area. Table 2 presents a summary of factors identified in related research.…”

Section: A Emergency Flight Planningmentioning

confidence: 99%

“…Reference Source Digital elevation map [2,11] Shuttle Radar Topographic Mission [3] U.S. Geological Survey, National Elevation Set Man-made structures/buildings [3] New York City Primary Land Use Tax-Lot Output [10] Ordinance Survey of Northern Ireland [11] OpenStreetMap Surface/terrain type [3] U.S. Geological Survey, National Land Cover Dataset [10] Ordinance Survey of Northern Ireland [11] OpenStreetMap…”

Section: Information Typementioning

confidence: 99%

“…Landing sites can be prioritized before flight planning [5,12] with subsequent focus on a single site, or sites can be prioritized with additional consideration of flight plan risks after the plan is computed [3,11,13]. Flight planning can be based on database, sensor, or combined information.…”

Section: Information Typementioning

confidence: 99%

“…Such risk can then be stored in a table or matrix format potentially updated before each flight for use by an onboard flight planner [3,11]. Benefits and limitations of the examined databases and proposed risk evaluation strategy are analyzed and illustrated with a case study over the Milan, Italy, metropolitan area, for which a mobile phone dataset is openly available.…”

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confidence: 99%

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Evaluating Risk to People and Property for Aircraft Emergency Landing Planning

Donato

Atkins

2017

Journal of Aerospace Information Systems

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General aviation and small unmanned aircraft systems are less redundant, may be less thoroughly tested, and are flown at lower cruise altitudes than commercial aviation counterparts. These factors result in a higher probability of a forced or emergency landing scenario. Currently, general aviation relies on the pilot to select a landing site and plan a trajectory, even though workload in an emergency is typically high, and decisions must be made rapidly. Although sensors can provide local real-time information, awareness of more distant or occluded regions requires database and/or offboard data sources. This paper considers different data sources and how to process these data to inform an emergency landing planner regarding risks posed to property, people on the ground, and the aircraft itself. Detailed terrain data are used for selection of candidate emergency landing sites. Mobile phone activity is evaluated as a means of real-time occupancy estimation. Occupancy estimates are combined with population census data to estimate emergency landing risk to people on the ground. Openly available databases are identified and mined as part of an emergency landing planning case study. = total number of country codes n g = total number of grid cells n t = total number of time intervals R a = risk based on landing area R h = risk to people on ground R ls = overall landing site risk R p = risk to property on ground R v = risk to vehicle t = time, min t 10 m = starting time of 10 min time interval, min W h , W p , W v , W a = weights associated with landing site risks w = A transition cost weight Θ = median of maximum aggregated SMS and call activity during a day, for a day of the week λ census = occupancy from census datâ λ census = normalized occupancy from census data λ rt = real-time occupancy estimation λ 0 = A fixed transition cost element Φ = median of aggregated SMS and call activity for each grid cell, day of the week and time interval of the day Φ grid = sum of Φ for all grid cellŝ Φ = normalized Φ ϕ = aggregated SMS and call activitŷ ϕ = normalized ϕ ϕ 1 = activity in terms of SMS-in ϕ 2 = activity in terms of SMS-out ϕ 3 = activity in terms of call-in ϕ 4 = activity in terms of call-out ϕ 5 = activity in terms of internet ϕ i = modified activity feature for heat map plot

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