Chromatic Aberration Distance Detection for UAV Navigation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Automated unmanned vehicles, particularly unmanned aerial vehicles (UAVs), face challenges in route guidance and collision avoidance due to the weight and mass limitations imposed by bulky and expensive equipment like radar systems, necessitating a more lightweight and cost-effective method for determining distances and navigating.
Innovation Solution
The system uses imaging technology to calculate distances by separating images into derivative images associated with different wavelengths, determining sharpness, and processing these images to estimate the distance between the vehicle and objects, allowing for 3D mapping and route adjustments without the need for bulky radar systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If radar systems are used for route guidance and collision avoidance, then navigation reliability is improved, but vehicle weight increases
Solution Approach 1:
The patent replaces radar systems (electromagnetic detection) with a camera-based chromatic aberration analysis system. The camera captures images and processes chromatic aberration effects to determine distance, substituting heavy radar equipment with lightweight optical components while maintaining collision avoidance functionality.
Solution Approach 2:
The system uses a standard camera (common consumer device) to perform distance measurement functions traditionally requiring specialized radar equipment. By capturing and analyzing chromatic aberration patterns in regular camera images, the system creates a lightweight copy of radar's distance detection capability without needing actual radar hardware.
2Measurement precision
If radar systems are used for distance measurement, then measurement precision is improved, but device cost increases
Solution Approach 1:
The patent employs inexpensive camera modules instead of costly radar systems. Standard camera sensors and lenses, which are mass-produced and inexpensive, are used to capture images for distance measurement, replacing expensive specialized equipment while achieving sufficient measurement precision for UAV navigation.
Solution Approach 2:
The system substitutes radar-based electromagnetic detection with optical camera-based detection. By processing chromatic aberration effects in captured images, the system achieves distance measurement precision comparable to radar but with significantly lower hardware costs and complexity.
3Weight of moving object
If chromatic aberration analysis is used for distance calculation, then device weight is reduced, but measurement precision may be affected
Solution Approach 1:
The patent converts chromatic aberration, traditionally considered an optical defect or harmful effect, into a useful measurement signal. By analyzing the differential focus and color separation caused by chromatic aberration across different wavelengths, the system extracts distance information, turning an optical imperfection into a precise ranging mechanism.
Solution Approach 2:
The system changes the measurement parameter from direct image sharpness to chromatic aberration characteristics. By analyzing how different wavelengths (colors) focus at different distances due to chromatic aberration, the system calculates object distance based on the pattern of color separation and focus variation, achieving accurate measurement with lightweight camera equipment.
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 accurate distance calculation and route guidance using commonly available camera devices, reducing weight and cost while enhancing navigation and collision avoidance capabilities in UAVs.
Implementation Method 1
Because each different wavelength has a different focal point with a given lens, the images will have a varying degree of sharpness
Data Source
AI summary
Described herein are systems and methods of determining a distance between an object depicted in an image and an imaging device that captured that image. In particular, the disclosure discusses that an image may be separated into multiple derivative images, each of which is associated with a different wavelength of light. In some embodiments, an image may be separated into images associated with wavelengths of primary colors (e.g., red, green, and blue). Once separate images have been created, a sharpness value may be determined for each image. A distance between the object and the imaging device may then be calculated based on sharpness values associated with each of the separate images.