Multi-Sensor Aircraft Docking with 3D Ramp Tracking
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Solution Overview
Problem
Current aircraft docking systems at airports face challenges such as accuracy issues in curved approaches, limited range detection, difficulty in detecting dark-colored aircraft, and malfunctioning in adverse weather conditions, leading to delayed and improper dockings, especially in complex multiple taxiway systems.
Innovation Solution
The implementation of a hybrid system combining camera image sensors and LIDAR/Radar sensors with data fusion processes to create a 3D model of the aircraft, providing precise guidance and identification, and enabling all-weather operation, along with auto-calibration and redundancy to ensure accurate and adaptable aircraft tracking and docking.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional laser/LIDAR systems are used for aircraft detection and tracking, then long range detection capability is provided, but the system fails in adverse weather conditions (thick snow, fog, heavy rain) and cannot detect dark colored aircraft
Solution Approach 1:
The patent combines multiple sensor types (laser/LIDAR sensors for long-range detection and camera sensors for visual confirmation) into a hybrid system. The laser sensors provide initial detection and tracking at long ranges, while camera sensors capture images for verification. This merging allows the system to leverage the strengths of each sensor type - the range capability of LIDAR and the all-weather reliability of cameras - to achieve both long-range detection and adverse weather reliability.
2Reliability
If camera systems are used for aircraft tracking, then all-weather operation is achieved, but the accuracy of distance measurements is insufficient for precise docking
Solution Approach 1:
The system merges camera sensors that provide all-weather operational reliability with laser/LIDAR sensors that deliver precise distance measurements. The camera component ensures the system operates reliably in various weather conditions by capturing visual data, while the laser component simultaneously provides accurate ranging information needed for precise docking guidance.
3Device complexity
If manual visual guidance is used for aircraft docking, then system complexity is minimized, but docking accuracy is insufficient leading to failed dockings and delays
Solution Approach 1:
The system provides automated docking guidance that reduces reliance on manual visual guidance by marshals. The hybrid sensor system automatically detects, tracks, and guides aircraft to the docking position, with the camera system providing visual confirmation and the laser system providing precise positioning data. This self-service capability improves docking accuracy while reducing the complexity of manual coordination.
4Extent of automation
If intrusive technologies like induction loops are used for docking guidance, then automated guidance is provided, but the system has significant limitations in adaptability and flexibility
Solution Approach 1:
The patent implements a universal docking guidance system using hybrid sensors that can adapt to multiple aircraft types, docking positions, and environmental conditions. The camera system provides universal visual detection capabilities, while the laser system provides universal ranging functionality. This multi-functional approach allows the system to serve various docking scenarios without requiring intrusive infrastructure like induction loops, thereby improving adaptability while maintaining automation.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This solution enhances safety and efficiency by enabling precise and repeatable aircraft guidance, reducing turnaround times, and accommodating various aircraft types and weather conditions, while minimizing maintenance requirements.
Implementation Method 1
receiving LIDAR/Radar sensor data of the aircraft
Implementation Method 2
receiving LIDAR/Radar sensor data of the aircraft
Implementation Method 3
receiving camera image data of the aircraft
Data Source
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AI summary
Tracking aircraft in and near a ramp area is described herein. One method includes receiving camera image data of an aircraft while the aircraft is approaching or in the ramp area, receiving LIDAR/Radar sensor data of an aircraft while the aircraft is approaching or in the ramp area, merging the camera image data and the LIDAR/Radar sensor data into a merged data set, and wherein the merged data set includes at least one of: data for determining the position and orientation of the aircraft relative to the position and orientation of the ramp area, data for determining speed of the aircraft, data for determining direction of the aircraft, data for determining proximity of the aircraft to a particular object within the ramp area, and data for forming a three dimensional virtual model of at least a portion of the aircraft from the merged data.