Drone Flight Routing Using Real-Time Wind Data to Cut Energy Use
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Solution Overview
Problem
Drones face increased energy consumption due to adverse weather conditions like headwinds and downdrafts during flight, and existing route planning methods rely on modeled weather data rather than real-time, localized wind conditions.
Innovation Solution
The use of turbine-generated and airborne drone-generated wind data to optimize flight routes, allowing drones to avoid adverse weather and engage favorable conditions, thereby reducing energy consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If drones use modeled weather data for route planning, then route planning can be performed in advance, but the accuracy of wind condition prediction is insufficient leading to higher energy consumption
Solution Approach 1:
The system performs preliminary route planning using available weather data, but continuously updates the route during flight based on real-time wind measurements from other drones. This allows the drone to benefit from advance planning while adapting to actual conditions encountered during flight, resolving the contradiction between early route determination and accurate wind condition prediction.
Solution Approach 2:
The system implements a feedback mechanism where wind data collected by one drone is shared with and used to update the flight routes of other drones. This creates a network effect where each drone benefits from the real-time measurements of others, continuously improving route accuracy and reducing energy consumption without requiring each individual drone to perform complex real-time measurements itself.
2Loss of time
If drones fly directly from launch to destination, then flight time is minimized, but energy consumption increases due to adverse weather conditions like headwinds and downdrafts
Solution Approach 1:
The system dynamically adjusts flight routes during operation based on real-time wind condition data. Instead of following a static predetermined path, drones can deviate from their initial route to exploit favorable wind conditions or avoid adverse conditions, optimizing the trade-off between flight time and energy consumption in real-time.
Solution Approach 2:
The system changes the flight path parameters (route coordinates, altitude, speed) based on encountered wind conditions. By adjusting these parameters in response to real-time data, the drone can minimize energy consumption while maintaining acceptable flight times, resolving the contradiction between direct routing and weather optimization.
3Measurement precision
If drones collect real-time wind data during flight, then route optimization accuracy improves, but the complexity of the system increases
Solution Approach 1:
The system uses the drones themselves as the measurement instruments. Each drone's flight characteristics and sensor data are used to infer wind conditions, eliminating the need for separate dedicated measurement devices. This self-service approach provides accurate real-time wind data without significantly increasing system complexity.
Solution Approach 2:
The system merges the measurement functions of multiple drones into a collective data set. By combining wind data from multiple sources, the system achieves high measurement precision without requiring any single drone to be overly complex. The distributed measurement approach shares the complexity burden across the fleet.
Data Source
AI summary
Methods and apparatus for reducing energy consumed by drones during flight are disclosed. A drone includes a housing, a motor, receiver circuitry carried by the housing, and a route manager. The receiver circuitry is to receive airborne drone-generated wind data from an airborne drone located in an area within which a segment of a flight of the drone is to occur. The airborne drone-generated wind data is to be determined by an inertial measurement unit of the airborne drone. The route manager is to generate a route for the flight of the drone based on wind data, the wind data including the airborne drone-generated wind data. The route is to be followed by the drone during the flight. The route manager is to select at least one portion of the route to cause the drone to be at least partially propelled by wind to reduce energy consumed by the drone during the flight.