3D Flight Path Construction Using Genetic Algorithm and Recursive Space Decomposition
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Solution Overview
Problem
Current Flight Management Systems (FMS) for aircraft lack the capability to efficiently re-plan a 3D flight path in real-time, especially when encountering unforeseen hazards like weather or military threats, due to computational constraints and the need to process large amounts of data quickly, while also ensuring the path is flyable and safe.
Innovation Solution
The system employs a genetic algorithm to refine a reference path by extracting free cells from terrain and obstacles, considering aircraft kinematic constraints and threat data, and uses recursive spatial decomposition techniques like MOctree to generate a safe and flyable flight path within seconds, even in constrained computational environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a real-time auto router processes large amounts of terrain and threat data to generate a safe 3D flight path, then the safety and survivability of the aircraft is improved, but the computational time and processing load increase beyond the capabilities of onboard embedded platforms
Solution Approach 1:
The patent divides the large search area (150 nm radius) into multiple smaller grid cells or sectors. This segmentation allows the system to process terrain and threat data in manageable portions rather than as one overwhelming dataset, enabling real-time processing on constrained embedded platforms while maintaining comprehensive coverage of the search space.
Solution Approach 2:
The system extracts only the critical data elements needed for path planning from the vast terrain and threat databases. By identifying and processing only the relevant features (such as key terrain obstacles and active threats) rather than all available data, the system reduces computational load while maintaining safety requirements.
2Reliability
If the auto router searches a large area (150 nm radius) to find a safe flight path, then the coverage and safety are improved, but the data processing volume increases beyond what can be completed within seconds
Solution Approach 1:
The system performs preliminary processing of terrain data and threat information before the actual path planning occurs. By pre-organizing data into grid cells, pre-identifying obstacle locations, and pre-calculating threat zones, the system reduces the computational workload during real-time path generation, enabling rapid processing of large search areas.
Solution Approach 2:
The patent implements a multi-pass or iterative search approach where the system first performs a coarse search to identify viable corridors, then refines the path in detail. This partial action strategy allows the system to cover the entire 150 nm radius area for safety while completing the detailed path planning within seconds by focusing computational resources on promising regions.
3Reliability
If the system generates a 3D flight path that avoids multiple threats and terrain obstacles, then the flyability and safety are improved, but the computational complexity exceeds the capabilities of onboard embedded platforms
Solution Approach 1:
The patent transitions from traditional 2D path planning to 3D flight path generation by incorporating vertical dimension considerations. The system processes terrain elevation data and threat altitude information to create three-dimensional flight corridors, enabling the aircraft to overfly or climb over obstacles and threats rather than merely circumventing them horizontally, thus reducing overall path complexity.
Solution Approach 2:
The system employs dynamic path refinement where the flight path is continuously adjusted based on real-time threat updates and aircraft state. Rather than computing a single static optimal path, the system generates adaptable path segments that can be rapidly recomputed as conditions change, reducing the overall computational complexity by breaking the problem into smaller dynamic sub-problems.
Data Source
AI summary
A present novel and non-trivial system, module, and method for constructing a flight path used by an avionics system are disclosed. A processor receives flight plan data and object data associated with terrain and obstacles. Free cells are extracted above the objects using a recursive space decomposition technique, and a reference path is formed through traversable free space determined from the availability of free cells. In an additional embodiment, threat data associated with hostile military weaponry and significant meteorological conditions could affect the availability of free cells. A genetic algorithm applying genetic operators which include mutators is employed with aircraft kinematic constraints to refine the reference path used to form a population of best path candidates. When a best path is reached after cycling through a re-generation process of path candidates, flight path data representative of the best path is generated and provided to at least one avionics system.