Aircraft Pose Determination via 3D Model Correlation
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Solution Overview
Problem
Current aircraft refueling processes require high skill and experience, especially during turbulence, and lack efficient automated systems for pose determination and positioning between vehicles, which is crucial for safe and precise refueling operations.
Innovation Solution
A system utilizing an imaging device, control unit, and 3D model database to automatically determine the pose of one aircraft relative to another, employing computer vision and algorithms like Maximum Mutual Information Correlator and Fitts correlator for precise positioning and control, enabling autonomous operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual control by pilot is used for maneuvering and station-keeping, then flexibility and adaptability are maintained, but skill requirement increases and difficulty of operation worsens
Solution Approach 1:
The system enables the aircraft to automatically determine its own pose and control its positioning without continuous manual intervention. The pose determination system and control system work autonomously to maintain station-keeping and execute refueling maneuvers, reducing pilot workload while maintaining operational capability
Solution Approach 2:
The patent replaces manual mechanical control with an automated system combining imaging devices, computer vision algorithms, and electronic control systems. The mechanical action of pilot manipulation is substituted by an integrated sensor-processing-actuation loop that automatically adjusts aircraft position and orientation
2Extent of automation
If automated pose determination system is implemented, then extent of automation improves, but device complexity increases
Solution Approach 1:
The imaging device serves multiple functions: capturing images for pose determination, tracking boom position, monitoring probe-boom connection status, and providing visual feedback for station-keeping. This multi-functionality reduces the need for separate specialized sensors while achieving comprehensive automation
Solution Approach 2:
The system creates a virtual model (pose estimation) of the physical aircraft state by processing images through computer vision algorithms. This digital copy of the aircraft's position and orientation enables automated control without requiring direct physical measurement of all state variables
3Measurement precision
If precise pose determination is achieved through image analysis, then measurement precision improves, but computational requirements and processing time increase
Solution Approach 1:
The system pre-loads 3D models of the aircraft and boom into memory before operation. During real-time operation, these pre-prepared models are directly compared with captured images using correlation algorithms, eliminating the need for complex real-time 3D reconstruction and reducing computational latency
Solution Approach 2:
The pose determination focuses on extracting and matching key feature points and critical geometric relationships from images rather than processing entire image datasets. This selective feature-based approach achieves sufficient precision for refueling operations while significantly reducing computational burden
Data Source
AI summary
A system and a method include an imaging device configured to capture one or more images of a first vehicle. A control unit is in communication with the imaging device. A model database is in communication with the control unit. The model database stores a three-dimensional (3D) model of the first vehicle. The control unit is configured to receive image data regarding the one or more images of the first vehicle from the imaging device and analyze the image data with respect to the 3D model of the first vehicle to determine a pose of the first vehicle.