Aerial Vehicle Flight Path Selection in Adverse Weather
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Aerial vehicles face risks during adverse weather conditions, such as thunderstorms, due to wind shears and communication losses, which can lead to structural damage or mission failure, especially when pilots lack experience or when vehicles must divert from their flight paths to avoid weather events.
Innovation Solution
Implementing a Machine Learning Maneuver (MLM) model and a maneuver decision engine on board the aerial vehicle to generate and evaluate flight paths based on weather sensor data, identifying paths with the least structural risk and generating flight control commands to navigate through adverse weather safely.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If aerial vehicles maneuver around thunderstorms to avoid wind shears, then structural damage is prevented, but mission completion time increases and productivity decreases
Solution Approach 1:
The system dynamically adjusts flight paths in real-time based on weather conditions. Instead of static avoidance routes, the MLM model continuously generates and evaluates multiple dynamic flight path options, allowing the vehicle to adaptively navigate around thunderstorms while minimizing deviation from the original mission path and reducing overall mission time extension.
Solution Approach 2:
The system performs preliminary weather analysis and flight path planning before entering adverse weather zones. The MLM model predicts potential thunderstorm locations and prepares optimal avoidance routes in advance, allowing the aerial vehicle to proactively adjust its trajectory rather than reactively diverting, thereby reducing mission completion time.
2Productivity
If experienced pilots manually maneuver through adverse weather, then mission completion time is reduced, but the risk of structural damage increases due to human error
Solution Approach 1:
The aerial vehicle autonomously performs weather analysis and flight path decision-making using the onboard MLM model and maneuver decision engine. The system serves itself by automatically evaluating multiple flight paths, assessing structural risk, and executing maneuvers without human intervention, thereby eliminating pilot error while maintaining rapid response to weather changes.
Solution Approach 2:
The system replaces human pilot judgment and manual control with an automated machine learning-based decision engine. The MLM model substitutes human cognitive processing with computational algorithms that rapidly analyze weather data and evaluate flight paths, providing consistent, error-free decisions while maintaining the speed and precision needed for safe maneuvering.
3Reliability
If multiple flight paths are evaluated with confidence scores, then the probability of selecting the safest path increases, but the computational time and processing requirements increase
Solution Approach 1:
The maneuver decision engine evaluates multiple flight paths but applies confidence thresholds to filter options. Instead of exhaustively analyzing every possible path, the system focuses computational resources on evaluating a subset of promising paths with confidence scores above certain thresholds, achieving high reliability without excessive processing time.
Solution Approach 2:
The system changes the parameter of evaluation depth based on situational context. When weather conditions are stable or confidence scores are high for initial paths, the system reduces detailed evaluation to maintain speed. When uncertainty increases or confidence scores are low, the system intensifies evaluation of alternative paths, dynamically adjusting computational effort to balance accuracy and processing time.
Data Source
AI summary
A machine learning maneuver model can be programmed to generate maneuver data identifying a plurality of flight paths for maneuvering an aerial vehicle through an adverse weather condition and a flight path confidence score for each flight path of the plurality of flight paths based on at least weather sensor data characterizing the adverse weather condition. The flight path confidence score can be indicative of a probability of successfully maneuvering the aerial vehicle through the adverse weather condition according to a respective flight path. A maneuver decision engine can be programmed to evaluate each flight path confidence score for each flight path relative to a flight path confidence threshold to identify a given flight path of the plurality of flight paths through the adverse weather condition that poses a least amount of structural risk to the aerial vehicle.