3D Wind Factor Routing for Urban Drone Flight Paths
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Solution Overview
Problem
Determining efficient and safe flight paths for unmanned aerial vehicles (UAVs) in urban environments is challenging due to varying wind conditions and building geometries, which affect local wind factors and can lead to increased travel time, energy consumption, and potential damage to cargo.
Innovation Solution
A method and apparatus that utilize three-dimensional map data and wind models to calculate link-level wind factors for UAV routing, integrating wind condition data into the routing process to optimize flight paths based on wind speed, direction, and altitude, allowing for real-time adjustments and route optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional routing methods are used for UAVs in urban environments, then the routing system is simple to implement, but the travel time increases and energy consumption increases due to not accounting for wind conditions
Solution Approach 1:
The system performs preliminary computation of wind factors for each road link and altitude combination before UAV routing. Wind factor databases are pre-calculated using 3D city map data and wind models, storing wind speed, direction, and other parameters for various altitudes. This preliminary action allows the routing system to quickly query and utilize wind information during route planning, reducing actual flight travel time without requiring complex real-time calculations during flight.
Solution Approach 2:
The system divides the urban airspace into discrete road links from the road network, with each link having specific wind factor characteristics. Different altitudes above each road link are assigned different wind factor values based on local 3D building geometries and wind flow patterns. This local quality approach allows the routing system to select optimal altitude and path combinations tailored to specific local wind conditions, minimizing travel time and energy consumption.
2Use of energy by moving object
If wind factors are not considered in routing, then the routing calculation is computationally simple, but energy consumption increases due to adverse wind conditions
Solution Approach 1:
The system pre-calculates wind factors including wind speed, direction, and other parameters for each road link and altitude combination using computational fluid dynamics models and 3D city maps. These wind factor databases are stored and can be quickly queried during routing. By performing this computationally intensive work beforehand, the system enables energy-efficient routing decisions without requiring complex real-time calculations that would increase operational computational complexity.
Solution Approach 2:
The system varies routing parameters such as altitude and path selection based on wind factor conditions. By changing the altitude parameter, the UAV can exploit favorable wind patterns at different heights above the city. The routing algorithm selects paths and altitudes that minimize energy consumption by taking advantage of wind assistance or avoiding adverse wind conditions, directly reducing UAV energy usage.
3Reliability
If basic routing without wind consideration is used, then the system is easy to operate, but cargo safety deteriorates due to exposure to adverse wind conditions
Solution Approach 1:
The system pre-computes comprehensive wind factor databases that include wind speed, direction, and safety-related parameters for all road links and altitudes. This preliminary preparation allows the routing system to automatically select safe paths and altitudes that avoid severe wind conditions, protecting cargo without requiring operators to manually assess complex wind data or make sophisticated safety decisions.
Solution Approach 2:
The wind factor database acts as an intermediary between the complex wind environment and the routing decision-making process. By pre-processing wind information into structured, queryable formats with safety metrics, the system mediates between environmental complexity and operational simplicity. The routing algorithm can directly query this intermediary database for safe paths, maintaining ease of operation while ensuring cargo safety through wind-aware route selection.
Data Source
AI summary
Embodiments include apparatus and methods for determining link level wind factors and providing routes for drones based on the wind factors. At least a portion of the route corresponds to airspace above a road network. Wind factor values are assigned to a range of altitudes of drone air space above a road link of the road network based on a wind model and stored in a database. The wind model is applied to a location based on wind condition data and three-dimensional (3D) features from 3D map data associated with the location. The route is optimized based on the determined wind factors.