Autonomous Runway Centerline Alignment Under Poor Visibility
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Solution Overview
Problem
Existing aircraft ground navigation at airports relies heavily on pilot skills and airport markings, lacking autonomous or semi-autonomous systems to ensure safe and precise navigation, especially in challenging conditions.
Innovation Solution
A system utilizing sensors and a computing system to process environmental data, providing control instructions for semi-autonomous or fully autonomous aircraft navigation, aligning with airport markings and avoiding obstacles, using cameras, radar, and LIDAR for environment mapping and obstacle detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If pilot-controlled navigation with airport markings is used, then navigation guidance is provided, but navigation precision and safety deteriorate in challenging conditions such as poor visibility or missing markings
Solution Approach 1:
The patent replaces the mechanical/pilot-dependent navigation system with an autonomous computer vision system. The system uses cameras to capture images of airport markings, processes these images through algorithms to detect centerlines and boundaries, and automatically generates navigation guidance without requiring pilot interpretation of visual markings.
Solution Approach 2:
The patent introduces an intermediary processing system between the airport markings and the aircraft navigation. This system includes image capture devices, processing units that analyze marking patterns, and guidance generation modules that translate detected markings into actionable navigation instructions, bridging the gap between static markings and dynamic navigation needs.
2Measurement precision
If autonomous navigation systems are implemented, then navigation precision improves, but system complexity increases
Solution Approach 1:
The patent divides the autonomous navigation system into distinct functional modules: image capture subsystem, image processing subsystem, marking detection subsystem, and guidance generation subsystem. Each module performs a specific function, making the overall complex system manageable through modular architecture where each segment can be independently developed, tested, and maintained.
3Measurement precision
If multiple sensors are used for environment mapping, then obstacle detection accuracy improves, but device complexity and cost increase
Solution Approach 1:
The patent combines multiple sensor types (cameras, radar, LIDAR) into an integrated sensing system that shares common processing infrastructure. The sensors work synergistically, with each sensor type compensating for the weaknesses of others, while the system uses unified processing algorithms to fuse data from all sensors, reducing overall system complexity compared to separate independent systems.
Data Source
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AI summary
Example implementations relate to autonomous airport runway navigation. An example system includes a first sensor and a second sensor coupled to an aircraft at a first location and a second location, respectively, and a computing system configured to receive sensor data from one or both of the first sensor and the second sensor to detect airport markings positioned proximate a runway. The computing system is further configured to identify a centerline of the runway based on the airport markings and receive sensor data from both of the first sensor and the second sensor to determine a lateral displacement that represents a distance between a reference point of the aircraft and the centerline of the runway. The computing system is further configured to control instructions that indicate adjustments for aligning the reference point of the aircraft with the centerline of the runway during subsequent navigation of the aircraft.