Aircraft Docking Sensor Fusion for All-Weather Ramp Tracking
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Solution Overview
Problem
Current aircraft docking systems face challenges such as inaccurate docking due to distance measurement errors, complex calibration requirements, and limitations in detecting aircraft in various weather conditions and with different aircraft types and colors. Additionally, these systems often lack redundancy and require frequent maintenance.
Innovation Solution
The proposed system combines camera image sensors and LIDAR/Radar sensors to create a robust detection, tracking, and docking system. This system uses data fusion to merge camera image data and LIDAR/Radar sensor data, enabling precise aircraft positioning and orientation, even in adverse weather conditions. It also includes redundancy to ensure continuous operation even if one of the sensor systems fails.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If camera systems are used for aircraft detection and tracking, then visual guidance can be provided to pilots, but distance measurement accuracy is insufficient and precise calibration requirements make the system difficult to set up and rely upon for accurate measurements
Solution Approach 1:
The patent combines camera systems with LIDAR systems to create a hybrid detection system. The camera provides visual guidance and image data while the LIDAR provides accurate distance measurements through time-of-flight calculations. This merging of systems resolves the contradiction by achieving high measurement precision through LIDAR while the camera system provides complementary visual information without requiring precise calibration for distance measurement.
Solution Approach 2:
The patent introduces LIDAR as an intermediary system that mediates between the camera's visual information and the need for accurate distance measurement. The LIDAR system acts as a mediator that provides precise range data through laser time-of-flight measurements, eliminating the need for complex calibration of the camera system for distance measurement while maintaining visual guidance capabilities.
2Length of stationary object
If laser/LIDAR systems are used for long range detection of aircraft, then detection range is extended, but they have issues with curved approach of aircraft, dark colored aircraft detection, and thick snow/foggy/heavy rain conditions
Solution Approach 1:
The patent merges camera-based detection with LIDAR-based detection to create a complementary system. The camera system provides reliable detection in adverse weather conditions and for dark-colored aircraft where LIDAR may struggle, while the LIDAR system provides accurate distance measurement and works effectively for long-range detection in clear conditions. This combination resolves the contradiction by ensuring reliable detection across all conditions through system complementarity.
Solution Approach 2:
The patent employs multiple LIDAR wavelengths and adjusts detection parameters based on environmental conditions. By changing the operational parameters of the LIDAR system (wavelength, power, pulse frequency) and combining with camera data, the system maintains long-range detection capability while improving reliability in adverse conditions such as fog, rain, and snow where different wavelengths penetrate differently.
3Device complexity
If single sensor systems are used for aircraft detection, then system simplicity is maintained, but redundancy is lacking and frequent maintenance is required when sensors malfunction or are obscured
Solution Approach 1:
The patent combines camera sensors with LIDAR sensors to create a multi-sensor system that provides inherent redundancy. Each sensor type can compensate for the other's failures or obscurations - if the camera is obscured, the LIDAR can continue detection, and vice versa. This merging maintains operational reliability without requiring overly complex single-sensor systems, resolving the contradiction between simplicity and redundancy.
Solution Approach 2:
The patent implements a multi-sensor architecture that provides beforehand cushioning against sensor failure or obscuration. By having both camera and LIDAR systems operating simultaneously, the system pre-prepares backup detection capabilities that activate automatically when one system degrades or fails, ensuring continuous operation without requiring complex maintenance interventions.
4Measurement precision
If automated docking guidance systems are implemented, then docking accuracy is improved, but the system complexity increases with multiple sensor types and data fusion requirements
Solution Approach 1:
The patent designs a unified data fusion architecture that processes both camera image data and LIDAR range data through a common computational framework. This multi-functional processing system handles multiple sensor types, data formats, and detection scenarios through a single integrated algorithmic approach, achieving high docking accuracy while managing system complexity through universal processing methods rather than separate specialized systems.
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
The system achieves accurate and repeatable aircraft guidance, improved safety through enhanced obstacle detection and apron mapping, and reduced aircraft turnaround times. It is capable of detecting and tracking aircraft in all weather conditions and for all types and subtypes of aircraft, with minimal 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
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.