UAV Scan Planning Around Obstructions for 3D Target Coverage
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Solution Overview
Problem
Current unmanned aerial vehicle (UAV) systems lack the capability for efficient, autonomous scanning of complex structures and environments, particularly in generating high-resolution 3D models in real-time, with limitations in handling concavities, irregular surfaces, and obstructions.
Innovation Solution
The implementation of a UAV system that autonomously scans three-dimensional targets by generating an initial lower-resolution 3D model, dynamically refining it in real-time through iterative updates, and adapting the scan plan to ensure comprehensive coverage and obstacle avoidance, using onboard processors and image sensors for navigation and image capture.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a UAV system performs autonomous scanning to generate high-resolution 3D models in real-time, then measurement precision and productivity are improved, but device complexity increases due to the need for onboard processors, image sensors, and dynamic scan plan adaptation
Solution Approach 1:
The scanning process is divided into multiple passes: an initial lower-resolution scan to generate a coarse 3D model, followed by refined scans that target specific areas needing higher resolution. This segmentation allows the system to achieve high measurement precision in critical areas while managing device complexity through progressive refinement rather than attempting full high-resolution scanning simultaneously across the entire target.
Solution Approach 2:
The system performs a preliminary low-resolution scan to generate an initial 3D model before conducting detailed high-resolution scanning. This preliminary action provides a framework that guides subsequent scanning operations, allowing the UAV to identify areas requiring higher resolution and adapt its scan plan accordingly, thereby reducing the overall computational burden and device complexity requirements.
2Measurement precision
If the UAV autonomously adapts scan plans in real-time to handle concavities and irregular surfaces, then measurement precision improves, but loss of time increases due to iterative updates and dynamic adjustments
Solution Approach 1:
The scan plan is made dynamic rather than static. The system continuously updates the scan plan based on real-time analysis of the generated 3D model, automatically adjusting flight paths and scanning parameters to adapt to discovered concavities and irregular surfaces. This dynamic adaptation improves measurement precision without requiring complete re-planning, thereby reducing time loss compared to traditional iterative approaches.
Solution Approach 2:
The system implements feedback loops where the generated 3D model is continuously analyzed to identify gaps in coverage or areas requiring higher resolution. This feedback drives automatic adjustments to the scan plan, allowing the UAV to focus computational resources on problematic areas while maintaining efficient scanning of well-covered regions, thus balancing measurement precision with scanning duration.
3Measurement precision
If the system performs iterative refinement of 3D models during flight, then measurement precision improves, but use of energy increases due to continuous processing and computation
Solution Approach 1:
Instead of performing complete iterative refinement of the entire 3D model at maximum resolution, the system applies partial refinement only to specific regions that require higher accuracy. This selective approach maintains measurement precision for critical areas while significantly reducing the computational energy required compared to full-model refinement, as the UAV processes only necessary portions at high detail levels.
Solution Approach 2:
The system dynamically changes processing parameters during flight, adjusting the resolution and refinement level based on the importance and accessibility of different target areas. For distant or difficult-to-reach features, lower resolution parameters are used to conserve energy, while close-up views of critical features receive higher resolution processing. This parameter adaptation maintains adequate measurement precision while optimizing energy consumption.
Data Source
AI summary
In some examples, one or more processors of an aerial vehicle access a scan plan including a sequence of poses for the aerial vehicle to assume to capture, using the one or more image sensors, images of a scan target. A next pose of the scan plan is checked for obstructions, and based at least on detection of an obstruction, the one or more processors determine whether a backup pose is available for capturing an image of the targeted point orthogonally along a normal of the targeted point. Responsive to determining that the backup pose is unavailable for capturing an image of the targeted point orthogonally along the normal of the targeted point, image capture of the targeted point is performed at an oblique angle to the normal of the targeted point.