Aerial Vehicle Landing Zone Verification With Dual Image Analysis
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Solution Overview
Problem
Current methods for controlling aerial vehicle flight movements for landing or cargo drop lack efficiency and reliability, particularly in determining clear landing or drop zones on the ground, as they rely solely on sensor signals without comprehensive environmental data analysis.
Innovation Solution
The method employs multiple camera devices on the aerial vehicle to record image data, using a combination of artificial intelligence-based and classical image analyses to determine clear landing or drop zones by overlapping zones identified by both methods, with position coordinates and release characteristics being used to control the vehicle's flight movement for precise landing or cargo drop.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a single sensor device is used to control aerial vehicle flight movement, then the device complexity is reduced, but the reliability of landing zone determination deteriorates
Solution Approach 1:
The image analysis function is segmented into two independent analysis paths: one using artificial intelligence and another using classical image analysis. Each path processes image data separately to determine landing zones, and both results are combined to achieve reliable landing zone determination. This segmentation allows the system to maintain high reliability while keeping each individual analysis module relatively simple.
Solution Approach 2:
The patent creates a redundant copy of the image analysis function through two different camera devices recording the same area, with each camera's data processed through separate analysis methods. This copying approach ensures that if one analysis path fails or produces incorrect results, the other can compensate, thereby improving reliability without requiring a single overly complex system.
2Measurement precision
If artificial intelligence-based image analysis is used alone, then the automation extent is increased, but the measurement precision of landing zone determination deteriorates
Solution Approach 1:
The patent merges two different image analysis approaches: artificial intelligence-based analysis and classical image analysis. The AI method provides automated processing and pattern recognition, while the classical method offers deterministic geometric calculations. By combining both methods and requiring their results to overlap, the system achieves both high automation and high precision in landing zone determination.
Solution Approach 2:
The system implements feedback by comparing the results of AI-based analysis with classical analysis. The classical analysis serves as a verification mechanism that provides feedback on the AI's determination, ensuring precision while maintaining automation. The overlap of both analysis results confirms the accuracy of the landing zone identification.
3Reliability
If multiple camera devices and dual image analysis methods are employed, then the reliability of clear zone determination is improved, but the loss of time in processing increases
Solution Approach 1:
The patent performs preliminary action by having multiple camera devices continuously record image data of the ground area before the aerial vehicle reaches the landing phase. This advance recording ensures that image data is already available and processed when needed, reducing the critical processing time during the actual landing sequence while maintaining high reliability through redundant analysis.
Data Source
AI summary
The preferred embodiments relate to a method for controlling a flight movement of an aerial vehicle for landing the aerial vehicle, including: recording of first image data by means of a first camera device, which is provided on an aerial vehicle, and is configured to record an area of ground, wherein the first image data is indicative of a first sequence of first camera images. The method also includes recording of second image data by means of a second camera device, which is provided on the aerial vehicle, and is configured to record the area of ground, wherein the second image data is indicative of a second sequence of second camera images.