Emergency Flight Planning for an Energy-Constrained Multicopter

Abstract

Small Unmanned Aircraft Systems (UAS) have diverse commercial applications. Risk mitigation techniques must be developed to minimize the probability of harm to persons and property in the vicinity of the aircraft. This paper presents an emergency flight planner combining sensor-based and map-based elements to collectively plan a landing path for a UAS that experiences an unexpected low energy condition while flying over a populated area. Focus is placed in this work on the use of public databases of population distribution, structure locations, and terrain to create an efficient-to-access cost map of the data. Safe landing plans are generated with an A* search algorithm shown to be feasible for real-time use with the cost map. Simulation-based case studies are presented of a quadrotor UAS operating within New York City to illustrate how different cost terms impact optimal path characteristics.

Evaluating Risk to People and Property for Aircraft Emergency Landing Planning

“…= 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 ϕ i avg = average activity in terms of feature i throughout the entire database ϕ grid = sum of ϕ for all grid cells I. Introduction T RIPLY redundant commercial transport aircraft are rarely forced to land off-runway, but emergency landings are occasionally required today in general aviation and experimental aircraft operations.…”

“…Such information, however, is restricted by sensor field of view, occlusion, and range constraints. Onboard databases can provide an emergency landing planner information over a much larger reachable landing area [3]. Data link enables updated information to be accessed in real time.…”

“…1 algorithm for exploiting onboard sensors, static database information, and real-time data link for emergency landing. This approach augments the decision strategy and static data proposed in [3] with data link and a list of specific landing sites vetted during preprocessing. In the case of an emergency, the onboard decision algorithm can fuse database and data-link information with locally acquired sensor data to decide between landing locally based on onboard sensor feedback versus flying to a known landing site beyond immediate sensor field of view but inside the reachable area or footprint, considering the aircraft's remaining range estimate.…”

“…This paper takes full advantage of open data to provide contextual information that provides a more informed risk assessment of candidate landing sites than in previous work [1,3,10]. The proposed method allows consideration of temporal (time-of-day) influence on the computed risk of each landing site, something recognized but not used in [3], and that has been addressed at only a basic level in [1]. Moreover, this paper proposes the use of mobile phone data to estimate risk in the case of special events that increase the number of people in the vicinity of candidate emergency landing sites.…”

“…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.…”