UAV Pose-Based Airflow Modeling for Urban Wind Routing
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Solution Overview
Problem
Current technologies face challenges in efficiently routing unmanned aerial vehicles (UAVs) in urban environments due to varying wind patterns and building geometries, which affect flight paths and increase energy consumption and travel time, and can result in damage to carried cargo.
Innovation Solution
A method and apparatus that model airflow by receiving sensor data from UAVs to determine pose and calculate wind vectors, generating an airflow model, and using this data to optimize UAV routes by selecting wind section areas based on wind factor values stored in a database, thereby reducing travel time and energy consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If UAVs fly in urban environments with varying wind patterns, then they can perform delivery and surveillance tasks, but their travel time and energy consumption increase
Solution Approach 1:
The system performs preliminary wind mapping by having UAVs fly through urban environments and collect sensor data to determine pose and calculate wind vectors. This wind information is stored in a database and used for future route planning, allowing subsequent UAVs to benefit from pre-collected wind data without experiencing the initial exploration overhead.
Solution Approach 2:
The system implements feedback by using actual wind measurements collected from UAV flights to optimize future routes. The wind information database is continuously updated with new measurements, and routing algorithms use this feedback to select paths that minimize unfavorable wind conditions, thereby reducing travel time and energy consumption for ongoing operations.
2Productivity
If UAVs fly in urban environments with varying wind patterns, then they can perform delivery and surveillance tasks, but energy consumption increases
Solution Approach 1:
The system performs preliminary wind mapping by having UAVs fly through urban environments and collect sensor data to determine pose and calculate wind vectors. This wind information is stored in a database and used for future route planning, allowing subsequent UAVs to benefit from pre-collected wind data without experiencing the initial exploration overhead.
Solution Approach 2:
The system implements feedback by using actual wind measurements collected from UAV flights to optimize future routes. The wind information database is continuously updated with new measurements, and routing algorithms use this feedback to select paths that minimize unfavorable wind conditions, thereby reducing travel time and energy consumption for ongoing operations.
3Productivity
If UAVs fly through areas with unfavorable winds and turbulence, then they can maintain direct routes, but cargo may be damaged
Solution Approach 1:
The system implements feedback by using actual wind measurements collected from UAV flights to optimize future routes. The wind information database is continuously updated with new measurements, and routing algorithms use this feedback to select paths that minimize unfavorable wind conditions, thereby reducing travel time and energy consumption for ongoing operations.
Solution Approach 2:
The system changes routing parameters based on wind conditions by selecting different wind section areas from the database. Instead of always taking the most direct geometric path, the system adjusts routes to avoid areas with unfavorable wind patterns, turbulence, or gusts that could compromise cargo safety, accepting longer paths when necessary.
Data Source
AI summary
Embodiments include apparatus and methods for modeling air flow from flight responses in aerial vehicles. Sensor data is received for aerial vehicles in a geographic area. The pose (e.g., roll, pitch, and yaw) of the aerial vehicles is calculated from the sensor data. One or more wind vectors are calculated based, at least in part, on the pose. An air flow model is generated from the wind vectors.