Projected Grid Intrusion Detection for UAV Landing
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Solution Overview
Problem
Current relative navigation systems for unmanned aerial vehicles (UAVs) lack the ability to detect intruding objects during landing operations, especially in environments without traditional landing aids, which limits their flexibility and safety.
Innovation Solution
A method and system that projects a grid into three-dimensional space using a grid generator and employs a detector module to identify intruding objects by detecting retro-reflected, backscattered, or blocked grid light, allowing for real-time detection and classification of potential hazards.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If traditional landing aids (ILS, VOR, DME, MLS, RADAR) are used to enable automated UAV landings, then landing automation and navigation capability are improved, but infrastructure complexity and deployment cost increase
Solution Approach 1:
The projected grid system serves multiple functions simultaneously: it provides navigation information to the UAV, enables relative position measurement, and acts as an obstacle detection mechanism. The same projected lines are used for both guiding the UAV during approach and detecting intruding objects in the landing zone, eliminating the need for separate infrastructure systems.
Solution Approach 2:
The system uses the navigation grid itself as the detection mechanism rather than requiring external sensors or separate detection infrastructure. The detector module analyzes the same projected lines that guide navigation, and the UAV's own movement through the grid provides the measurement data needed for both navigation and obstacle detection functions.
2Adaptability or versatility
If relative navigation systems operate without traditional landing aids to improve flexibility and deployability, then adaptability to various locations is improved, but ability to detect intruding objects during landing deteriorates
Solution Approach 1:
The projected grid functions dually as both the navigation reference and the obstacle detection mechanism. The same set of projected lines that enable the UAV to determine its position and navigate to the landing zone also serve as the basis for detecting intruding objects, providing both navigation and safety functions without requiring additional infrastructure.
Solution Approach 2:
The projected lines act as an intermediary between the navigation function and the detection function. By analyzing how the UAV interacts with these lines (intersection patterns, timing, sequence), the system derives both navigation information and obstacle detection data from the same intermediary reference structure.
3Measurement precision
If the detector module analyzes multiple grid line characteristics (intersection patterns, timing, sequence) to improve intrusion detection accuracy, then measurement precision is improved, but computational complexity increases
Solution Approach 1:
The detection process is segmented into distinct analytical components: identifying grid line intersections, measuring timing between intersections, determining sequence of intersections, and analyzing spatial patterns. Each segment processes a specific aspect of the grid-UAV interaction data, making the overall complex detection task manageable through systematic breakdown into discrete measurement steps.
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
Enhances safety by enabling the detection and classification of intruding objects within the field of regard, improving situational awareness and allowing for safer navigation and landing operations, even in environments without traditional landing infrastructure.
Implementation Method 1
detecting retro-reflected or backscattered grid light reflected from the intruding object by a detector module located at or near the grid generator
Implementation Method 2
detecting retro-reflected or backscattered grid light reflected from the intruding object by a detector module located at or near the grid generator
Implementation Method 3
detecting a multi-path reflection of at least a portion of the projected grid deflecting off the intruding object, by a detector module located on an intended receiver of the grid
Implementation Method 4
detecting that a portion of the projected grid is blocked or detecting a reduction in light intensity from the grid by a detector module located on an intended receiver of the grid
Data Source
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AI summary
A relative navigation system and methods of intrusion detection for such a relative navigation system where the method includes repeatedly projecting from a grid generator 10 a set of lines defining a grid into three dimensional space to define a field bounded by the grid and encoded with grid data configured to identify predetermined points on the grid and determining a presence of an intruding object within the field.