Dual-Camera Runway Alignment for Autonomous Takeoff Control
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Solution Overview
Problem
Current methods for aligning an aircraft with a runway centerline during takeoff, such as those using neural networks, are inefficient and prone to errors, especially when the runway has an atypical appearance, requiring significant computing resources and burdensome pre-operation calibration.
Innovation Solution
A computing device that uses dual cameras mounted on opposite sides of the aircraft to capture images, determining angles between marked lines on the runway and reference lines, and adjusts the control surface to align the aircraft with the centerline, consuming less computing resources and reducing calibration needs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If neural networks or machine learning techniques are used for runway alignment, then the aircraft can maintain centerline alignment during takeoff, but the computing device requires significant computing resources and extensive pre-operation training with thousands of images
Solution Approach 1:
The patent replaces complex neural network-based image analysis with a simpler geometric method using perspective projection principles. Instead of training deep learning models to recognize runway features, the system uses mathematical transformations based on camera geometry and known runway markings to directly calculate alignment information, significantly reducing computing resource requirements while maintaining alignment reliability
Solution Approach 2:
The patent uses readily available off-the-shelf cameras rather than specialized or expensive imaging equipment. The approach leverages standard image processing techniques that can be implemented with inexpensive computational resources, avoiding the need for high-performance computing hardware required by neural network approaches
2Adaptability or versatility
If neural networks are used for runway alignment, then the aircraft can adapt to different runway appearances, but the system may misidentify runway centerlines in cases with atypical runway appearances and requires extensive training data
Solution Approach 1:
The patent changes the approach from learning-based parameter adaptation to geometry-based parameter calculation. By using perspective projection mathematics and known relationships between camera position, runway marking geometry, and image coordinates, the system reliably identifies runway centerlines through deterministic geometric relationships rather than probabilistic neural network predictions, eliminating misidentification issues with atypical runways
3Extent of automation
If neural networks are used for runway alignment, then the system can learn from training data, but the pre-operation calibration process is burdensome and time-consuming
Solution Approach 1:
The patent performs preliminary geometric calibration during manufacturing by mounting cameras at precise, predetermined positions and orientations on the aircraft. This pre-established geometric relationship between cameras and aircraft body allows the system to immediately calculate runway alignment using perspective projection without requiring time-consuming on-site training or calibration operations
Solution Approach 2:
The system uses its own geometric configuration and the inherent perspective projection physics to automatically determine alignment without external training data or calibration procedures. The mathematics of perspective projection naturally provides the alignment information when combined with known camera parameters and runway marking geometry, making the system self-sufficient
Data Source
AI summary
Described herein is an example method for aligning an aircraft with a centerline of a runway during takeoff. The method includes accessing a first image captured by a first camera mounted on a first side of the aircraft; accessing a second image captured by a second camera mounted on a second side of the aircraft that is opposite the first side; determining a first angle between a first marked line on the runway in the first image and a first reference line in the first image; determining a second angle between a second marked line on the runway in the second image and a second reference line in the second image; and based on the first angle and the second angle, moving a control surface of the aircraft such that the aircraft moves closer to the centerline of the runway.