UAS Traffic Management Using Building-Resolved Atmospheric Modeling
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Solution Overview
Problem
Current Unmanned Aircraft Systems (UAS) traffic management systems lack the ability to dynamically respond to environmental changes and airspace demand in real-time, leading to inefficiencies and safety concerns due to inadequate prediction of near-surface winds and turbulence, particularly in urban environments.
Innovation Solution
The described Small Aircraft Traffic Management System (SATMS) integrates detailed wind and turbulence predictions using a building-resolved atmospheric model and graph analytics to calculate and select safe, efficient routes for UAS, employing a Weighted Graph approach with Dijkstra's algorithm to optimize route planning and scheduling, ensuring aircraft spacing and minimizing delays.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional UTM systems use standard weather prediction models, then system complexity is reduced, but the precision of near-surface wind and turbulence prediction deteriorates
Solution Approach 1:
The patent segments the atmosphere into multiple vertical layers (surface layer, elevated layer, free atmosphere) and applies different modeling approaches to each layer. The building-resolved atmospheric model specifically targets the surface layer where sUAS operate, while larger-scale models handle upper layers, optimizing precision where needed without unnecessary complexity throughout the entire system.
Solution Approach 2:
The patent implements local quality by applying building-resolved atmospheric modeling specifically in urban environments where sUAS operations occur, rather than uniformly across all airspace. This localized approach provides high-precision wind and turbulence predictions in the critical operational zone without the computational burden of applying the same level of detail globally.
2Reliability
If real-time environmental data processing is implemented, then route safety and efficiency are improved, but computational time and processing complexity increase
Solution Approach 1:
The patent performs preliminary action by pre-processing environmental data and pre-calculating atmospheric conditions along potential flight paths before sUAS departure. The system continuously updates wind and turbulence predictions in advance, allowing real-time route optimization without requiring intensive computational processing during critical decision-making moments.
Solution Approach 2:
The patent implements dynamics by continuously updating environmental data and re-calculating optimal routes in real-time as sUAS progress through their flights. The system dynamically adjusts flight paths based on changing weather conditions, maintaining high reliability without static, pre-planned routes that would become obsolete.
3Measurement precision
If building-resolved atmospheric modeling is used, then positional uncertainty prediction is improved, but model complexity and data processing requirements worsen
Solution Approach 1:
The patent segments the atmospheric modeling domain by applying building-resolved models only in urban areas with high-rise buildings where sUAS operations occur, while using coarser-resolution models in rural or open areas. This segmentation provides high positional uncertainty prediction accuracy where needed while reducing overall model complexity and computational requirements.
4Productivity
If graph analytics with Dijkstra's algorithm are employed for route optimization, then aircraft throughput and spacing efficiency are improved, but computational complexity for real-time processing increases
Solution Approach 1:
The patent applies preliminary action by pre-generating the graph structure representing the airspace, flight corridors, and potential routes before sUAS traffic management begins. The Weighted Graph with nodes and edges is constructed in advance, allowing Dijkstra's algorithm to efficiently compute optimal routes in real-time without the computational burden of building the graph structure during flight operations.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The SATMS effectively deconflicts UAS routes in real-time, optimizing aircraft throughput and safety by accounting for positional uncertainty due to meteorological factors, reducing delays, and ensuring safe separation in dynamic and complex urban environments.
Implementation Method 1
The processor is configured to: receive environmental data for a domain, the environmental data determined based on operational weather prediction models or weather observations; process the environmental data through an atmospheric model of the domain to determine mesoscale and microscale weather forecast data for the domain and a positional uncertainty of the sUAS vehicles within the domain
Implementation Method 2
The described system integrates detailed predictions of near surface windspeed and turbulence in a graph setting to calculate and select aircraft routes and provide flight scheduling guidance for UTM operations
Implementation Method 3
In some embodiments, the model includes a large eddy simulation (LES) atmospheric model to translate local time-varying weather observations (e.g., urban meteorological stations) or large-scale time-varying environmental conditions from a weather prediction model
Data Source
AI summary
Systems and methods for deconflicting small Unmanned Aircraft Systems (sUAS) within a domain. One example system includes a plurality of UAM/AAM vehicles; and a processor configured to: receive environmental data for a domain, the environmental data determined based on operational weather prediction models or weather observations; process the environmental data through an atmospheric model of the domain to determine mesoscale and microscale weather forecast data for the domain and a positional uncertainty of the sUAS vehicles within the domain; generate a Weighted Graph that describes and spans a plurality of possible sUAS vehicle routes over the domain; determine a route and a corresponding flight schedule for at least one of sUAS vehicles within the domain based on the Weighted Graph; and provide the route and the corresponding flight schedule to the at least one of the sUAS vehicles.