Aerial Vehicle Energy Map Generation for Flight Route Optimization
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Solution Overview
Problem
Aerial vehicles face inefficiencies in flight operations due to unpredictable changes in energy levels caused by natural atmospheric conditions, which existing technologies fail to effectively account for, leading to suboptimal route planning and energy management.
Innovation Solution
The system detects changes in an aerial vehicle's energy levels not attributed to operational changes, attributes these changes to naturally present energy sources or sinks, and uses this information to generate maps for efficient flight planning, allowing the vehicle to exploit energy surpluses or avoid energy deficits by navigating through regions with trained machine learning tools.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If traditional flight operations are used without considering natural energy sources, then flight operations are simpler to plan, but energy efficiency deteriorates due to unpredictable energy changes
Solution Approach 1:
The system performs preliminary mapping of natural energy sources and sinks in the operational region before flight operations. Energy maps are generated in advance showing locations of energy surpluses and deficits, allowing flight planners to pre-determine optimal routes that exploit these natural energy features, thereby improving energy efficiency without adding operational complexity
Solution Approach 2:
Energy maps serve as an intermediary between natural atmospheric conditions and flight planning decisions. The maps translate complex atmospheric energy variations into usable spatial information, enabling operators to make informed routing decisions without directly analyzing atmospheric data during flight operations
2Loss of energy
If flight routes are planned to exploit energy sources, then energy expenditure is reduced, but route planning becomes more complex
Solution Approach 1:
Energy maps are generated in advance of flight operations, identifying optimal routes that exploit natural energy sources. This preliminary route planning allows operators to minimize energy expenditure without requiring complex real-time decision-making during flight, as the energy-optimized paths are predetermined based on pre-collected atmospheric data
Solution Approach 2:
The system creates simplified representations (energy maps) of complex atmospheric energy distributions. These maps copy the essential energy features of the operational region in an easily interpretable format, allowing operators to plan energy-efficient routes without dealing with the full complexity of atmospheric physics
3Power
If natural energy sources are utilized for flight operations, then propulsion requirements are reduced, but detection and measurement of energy sources becomes more difficult
Solution Approach 1:
The system uses energy maps as an intermediary to detect and measure natural energy sources. Rather than directly measuring atmospheric energy parameters during flight, the system pre-maps energy sources and sinks using ground-based or preliminary aerial surveys, translating difficult-to-detect atmospheric features into usable spatial information for route planning
Solution Approach 2:
Detection and measurement of natural energy sources are performed in advance of flight operations through preliminary mapping missions or ground-based measurements. This preliminary detection allows the system to identify and catalog energy sources and sinks before operational flights, reducing the need for complex real-time detection systems during actual 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
This approach enables aerial vehicles to optimize their flight paths by identifying and utilizing energy-rich areas and avoiding energy-depleted zones, thereby enhancing operational efficiency and reducing energy expenditure.
Implementation Method 1
lift is generated when an airfoil passes through air, diverting the air and changing air pressure levels above and below the airfoil. Air flowing above an airfoil expands, and air flowing below the airfoil contracts, resulting in reduced air pressure above the airfoil and increased air pressure below the airfoil
Data Source
AI summary
Changes in energy of an aerial vehicle that are unrelated to any operational changes in the aerial vehicle may be associated with energy sources or sinks naturally present at a location. Air flows generated due to contrasts in surface temperatures or terrain features at locations may cause energy levels of aerial vehicles to rise or fall. Locations of changes in energy may be recorded and used to generate a map or other representation of energy within an area. The map or other representation may be used in selecting optimal routes for aerial vehicles within the area. Additionally, a machine learning system may be trained using maps or representations of energy within areas and images of such areas. An image of an area may be provided to a trained machine learning system as an input, and a representation of energy within the area may be generated based on an output.