UAV Contour Scanning for Complex 3D Surface Coverage
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Solution Overview
Problem
Conventional unmanned aerial vehicles (UAVs) face challenges in capturing images of complex scan targets with concavities, irregular surfaces, and asymmetric geometries efficiently and accurately, requiring manual piloting and lacking autonomous capabilities for thorough and repeatable scanning.
Innovation Solution
The UAV is configured to autonomously scan three-dimensional targets by dividing them into slices and determining virtual contours, using a coordinate system to traverse these contours at a selected distance, capturing images methodically and generating a 3D model, with the ability to adjust speed and pattern based on camera capabilities and desired detail.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual piloting is used to capture images of complex targets, then the pilot can adjust to various conditions, but the process becomes tedious and time-consuming
Solution Approach 1:
The UAV system performs autonomous scanning operations by automatically dividing the target into slices, determining virtual contours, calculating flight paths, and capturing images without continuous human intervention. The system serves itself by using its own sensor data to generate scanning plans and execute autonomous navigation, thereby reducing pilot effort while maintaining high scanning efficiency
Solution Approach 2:
The system performs preliminary processing of target data to divide the complex target into manageable slices and pre-calculates virtual contours and flight paths before actual image capture. This preliminary action prepares the scanning plan in advance, enabling efficient autonomous execution without tedious manual adjustments during the scanning process
2Measurement precision
If conventional UAV scanning is used on complex targets with concavities and irregular surfaces, then the UAV can reach various positions, but the coverage accuracy and completeness deteriorate
Solution Approach 1:
The system divides the complex target into multiple slices along a primary axis, transforming a difficult three-dimensional scanning problem into a series of simpler two-dimensional contour tracking problems. Each slice is processed independently with its own virtual contour and flight path, enabling accurate coverage of complex geometries including concavities and irregular surfaces
Solution Approach 2:
The system introduces a dimensional transformation by creating virtual contours in a two-dimensional plane that represent three-dimensional target surfaces. The UAV flies along these 2D contours at controlled distances, effectively adding a distance dimension to achieve accurate 3D surface mapping while maintaining simpler 2D navigation control
3Adaptability or versatility
If the UAV captures images at a fixed distance from the target surface, then the imaging conditions are consistent, but the ability to adapt to varying target geometries is reduced
Solution Approach 1:
The system dynamically adjusts the UAV's flight path and imaging parameters based on real-time target geometry while maintaining consistent imaging conditions. The virtual contour methodology allows the flight path to adapt to varying target shapes, and the system automatically adjusts capture intervals and distances to ensure consistent image quality across different geometries
Data Source
AI summary
In some examples, an unmanned aerial vehicle (UAV) may determine a plurality of contour paths spaced apart from each other along at least one axis associated with a scan target. For instance, each contour path may be spaced away from a surface of the scan target based on a selected distance. The UAV may determine a plurality of image capture locations for each contour path. The image capture locations may indicate locations at which an image of a surface of the scan target is to be captured. The UAV may navigate along the plurality of contour paths based on a determined speed while capturing images of the surface of the scan target based on the image capture locations.