Aerial Vehicle Landing Zone Control With Redundant Image Analysis
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Solution Overview
Problem
Existing methods for controlling the flight movement of aerial vehicles for landing or dropping cargo are inefficient and unreliable due to the lack of effective ground condition assessment, particularly in determining clear landing or drop zones that are free from obstacles.
Innovation Solution
Employing multiple camera devices on the aerial vehicle to record image data, utilizing both AI-based and non-AI image analysis to redundantly determine clear landing or drop zones, and using position coordinates for precise control of the vehicle's flight movement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a single image analysis method is used to determine landing zones, then the processing is simple and fast, but the reliability and accuracy are insufficient
Solution Approach 1:
The image analysis process is segmented into two independent analysis paths: a first image analysis (e.g., AI-based) and a second image analysis (e.g., rule-based or traditional computer vision). Each analysis independently evaluates the same image data to determine landing zones, ensuring that the system does not rely on a single analysis method. This segmentation increases reliability by cross-validating results while maintaining manageable complexity through modular processing.
Solution Approach 2:
Different analysis methods are applied to different aspects of landing zone evaluation. The first image analysis may focus on semantic understanding and object recognition, while the second image analysis focuses on geometric validation and constraint checking. This local quality approach allows each analysis method to excel at its specialized task, improving overall reliability without requiring a single overly complex system.
2Measurement precision
If multiple camera devices and redundant image analysis are employed, then the accuracy and reliability of landing zone identification improve, but the processing time and computational load increase
Solution Approach 1:
The system performs preliminary filtering and preprocessing of image data from multiple camera devices before conducting the full redundant analysis. Basic validations such as image quality checks, coordinate system alignments, and obvious outlier detections are performed first, reducing the computational burden on the subsequent detailed analyses. This preliminary action ensures that the redundant image analyses work with pre-validated data, maintaining precision while reducing processing time.
Solution Approach 2:
The system implements a tiered analysis approach where the first and second image analyses perform different levels of scrutiny. In normal conditions, both analyses are executed fully for maximum precision. However, the system can adapt by performing only essential checks when time is critical, or by applying excessive analysis only to critical decision points. This partial or excessive action allows the system to balance precision requirements with time constraints dynamically.
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.