1,720,975 research outputs found
Effects of Traffic Density on Aircraft Collision Rate in Uncontrolled Airspace
No full textThis work utilises large numbers of Agent Based Simulations (ABS) to quantify the collision rate between unconstrained aircraft agents moving between stochastically generated endpoints in straight lines. Each ABS run is used as a sample for a Monte Carlo estimator of the collision rate for a given traffic density with quantified uncertainty.</span
Effectiveness of See and Avoid for avoiding small Uncrewed Aircraft
No full textThis explores the relationships between variables involved in the visual separation aerial environment (AKA See and Avoid). This is a cooperative principle that the vast majority of General Aviation uses in uncontrolled airspace to maintain separation.</span
A Correlational Analysis of Near Mid Air Collisions using Historical Surveillance Data
No full textThis work aims to test correlations between the 3D locations of Near Mid Air Collisions and the traffic conditions in the same area in order to identify future hazardous regions where NMACs are more likely to occur. This provides avenues to improve airspace design for fewer such events.</span
SEEDPOD Ground Risk: A Python application and library for Uncrewed Aerial Systems ground risk analysis and risk-aware path finding
No full textFull Strike and Fatality risk maps given areas. Currently the parameters for the wind etc, are not patched through to UI. CUDA implementation now used preferentially to CPU bound impl for risk map generation. This relies on a supported Nvidia GPU and working install of CUDA toolkit. This is checked for by the existence of a CUDA_HOME environment variable pointing to the install. Command Line Interface created and documented, however wheels need to built from source due to local dependencies not being possible to pre package
Uncrewed aerial systems operational risk analysis
No full textUncrewed Aircraft present an ever growing number of use cases from remote sensing to logistics with projections for large numbers in the skies of the future. Segregating airspace especially for their operations is neither sustainable nor scalable particularly in and around urban areas with existing airspace and airport infrastructure. Integration of UAS into unsegregated airspace requires robust risk assessment in order to prevent exposure of existing airspace users to additional undue risk. This is referred to as "Air Risk". A large number of use cases involve flight over urban areas, exposing third parties on the ground to additional risk from overflight. A majority of UAS do not require costly and stringent airworthiness certification akin to commercial passenger aircraft, therefore operational risk mitigation measures must be applied to ensure safety of operations to a suitable Target Level of Safety (TLS). This is referred to as "Ground Risk". Quantitative and objective risk assessment methodologies specific to time and three dimensional space are developed for parametrised UAS, accounting for both spatiotemporal population movement in real world settings and aerial traffic patterns derived from surveillance data. Such methods allow for the evaluation of probability of undesirable events such as third party ground fatalities and mid air collision to form an overall value for the risk posed by the UAS at a given position and time. Evaluation for large areas with a given TLS enables the objective determination of safe regions for UAS operations. As a probabilistic problem using primarily a sampling-based methodology with a large number of factors, a computational problem in terms of trade-off between resolution and computational time is present. Monte Carlo methods and Rare Event Simulation techniques are applied for estimation of air risk, whilst parallel computing and use of GPUs are shown to reduce computational time significantly for ground risk estimation. The end goal is a holistic, objective and quantitative risk assessment methodology for determination of the safety of UAS operations
Spatiotemporal ground risk mapping for uncrewed aerial systems operations
No full textIn this paper we propose the use of spatiotemporal population density data in the analysis of ground risk posed by UAS (Uncrewed Aerial System) operations. The spatiotemporal population density maps are generated through the combination of authoritative data sources, open source geospatial databases, and past works to dynamically classify proportions of a population to their expected daily activities based upon a given time. This adds a further dimension to analysis allowing evaluation and optimisation of ground risk, both spatially and temporally. This approach is used to analyse the ground risk posed under ballistic and gliding descents of a parameterized UAS along a case study path. An open source tool is implemented as part of this work to aid the decision making of operators and promote safer UAS operations
Quantifying specific operation airborne collision risk through Monte Carlo simulation
No full textIntegration of Uncrewed Aircraft into unsegregated airspace requires robust and objective risk assessment in order to prevent exposure of existing airspace users to additional risk. A probabilistic Mid-Air Collision risk model is developed based on surveillance traffic data for the intended operational area. Simulated probable traffic scenarios are superimposed on a desired Uncrewed Aircraft operation and then sampled using Monte Carlo methods. The results are used to estimate the operation-specific collision probability with known uncertainty in the output. The methodology is demonstrated for an example medical logistics operation in the United Kingdom, and a Target Level of Safety is used as a benchmark to decide whether the operation should be permitted
Spatiotemporal ground risk mapping for uncrewed aircraft systems operations
No full textIn this paper we propose the use of spatiotemporal population density data in the analysis of ground risk posed by uncrewed aircraft system (UAS) operations. The spatiotemporal population density maps are generated through the combination of authoritative data sources, open source geospatial databases, and past works to dynamically classify proportions of a population to their expected daily activities based upon a given time. This adds a further dimension to analysis allowing evaluation and optimization of ground risk, both spatially and temporally. This approach is used to analyze the ground risk posed under ballistic and gliding descents of a parameterized UAS along a case study path. An open source tool is implemented as part of this work to aid the decision making of operators and promote safer UAS operations
Drones: the scope for integration into multi-modal urban logistics services
No full textUncrewed Aerial Vehicles (UAVs, or drones) are seen as a potential new logistics mode, both for urban areas and beyond, that could reduce service times, energy consumption, tailpipe atmospheric emissions, and numbers of van/truck-based trips, whilst also improving accessibility in hard-to-reach locations. Drones have been used successfully across many sectors from surveillance and security to photography and surveying, inspection of infrastructure and agriculture, aid provision, and environmental monitoring. Most of these activities involve flights within Visual-Line-of-Sight (VLOS), where the operator retains visual contact with the drone at all times. In contrast, large-scale commercial drone logistics services (i.e., payload delivery) require flights Beyond-Visual-Line-of-Sight (BVLOS) that entail more risk and require specific permissions, particularly in densely populated urban areas, which are key reasons why such services are not prevalent except for some medical use cases in Africa.With a particular focus on medical use cases, and using first-hand experience of operating BVLOS flights, this chapter will discuss the practical realities of integrating drones into existing urban logistics supply chain infrastructures, specifically:i) Public acceptance of drones for urban logistics purposes.ii) Payload capabilities of drones relative to the service demand.iii) Adherence to client quality assurance requirements when transporting sensitive payloads.iv) Implications of dangerous goods regulations for drone payloads.v) Implications of air and ground risks on route planning and optimisation.vi) Mechanisms for integrating drones alongside crewed aircraft in shared airspace.vii) Overall service reliability given weather conditions and minimal risk routing.viii) Cost implications of utilising drones as part of multi-modal urban logistics supply chains.<br/
Are Drones Safer Than Vans?: A Comparison of Routing Risk in Logistics
No full textDrones are being considered as an alternative transport mode to ground based van networks. Whilst the speed and application of such networks has been extensively studied, the safety aspects of such modes have not been directly compared. Using UK Department for Transport data and a drone flight planning approach using a probabilistic risk model, an estimation of fatality rates for seven origin-destination (O-D) pairs was undertaken in a theoretical case study of medical deliveries in the Southampton area of the UK. Using failure rates from the literature, results indicated that commercial vehicles (<3.5 T) were safer than drones in all cases by ≤12.73 (12.73 times more fatalities by drone than by road). With the O-D pairs covering a range of localities, routes covering more mileage on minor roads were found to be the least safe but were still ≥1.87 times safer than drone deliveries. Sensitivity tests on the modelled drone failure rates suggested that the probability of a failure would have to be ≤5.35×10−4 per flight-hour for drone risk to be equal to van risk. Investigating the circuity of drone routes (how direct a route is) identified that level of risk had a significant impact on travel distances, with the safest paths being 273% longer than the riskier, straight-line flight equivalent. The findings suggest that the level of acceptable risk when designing drone routes may negatively impact on the timeliness of drone deliveries due to the increased travel distance and time that could be incurred
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