Dynamic Grid Route Planning for Feasible Flight Paths
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Solution Overview
Problem
Conventional route planning algorithms using regular rectangular grids often generate routes that vehicles, especially aircraft, cannot follow due to restrictive movement operations and altitude limitations.
Innovation Solution
A method and apparatus that generate a grid by specifying start nodes and movement operations, iteratively adding edges and nodes to create a path that reflects feasible flight routes, allowing for different movement operations and cost functions for each stage of the route, optimizing the path using algorithms like A* to ensure the route is flyable and efficient.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a regular rectangular grid is used to define route nodes, then the route planning problem becomes finite and solvable, but the generated routes are not flyable by the vehicle
Solution Approach 1:
The route planning process is segmented into multiple stages, where each stage corresponds to a specific flight phase (e.g., takeoff, cruise, landing). Different movement operations are applied at different stages, allowing the system to generate flyable routes by matching vehicle capabilities with route requirements at each segment of the journey.
Solution Approach 2:
The grid structure is made dynamic by allowing different movement operations at different stages of the route. Instead of using a static regular rectangular grid with uniform movement constraints, the system adapts the grid generation process to reflect the vehicle's changing capabilities and constraints throughout the flight, ensuring generated routes are actually flyable.
2Speed
If a coarse grid is used to produce a small finite space, then computation is faster, but route precision and detail are reduced
Solution Approach 1:
The route planning space is segmented into multiple stages with different grid resolutions. Coarse grids are used for early stages (takeoff, initial climb) where high precision is less critical, while finer grids are applied to later stages (cruise, approach, landing) where precise route following is more important. This segmentation allows the system to balance computation speed with route precision across different flight phases.
Solution Approach 2:
Different grid densities are applied locally to different regions of the flight space based on their importance. Areas requiring high precision (such as approach and landing zones) use finer grids, while less critical areas use coarser grids. This local quality adjustment optimizes both computation speed and route precision where needed.
Data Source
AI summary
Disclosed is a method and apparatus for determining a route for a vehicle (2). The method comprises generating, by a processor (12), a grid (16) by specifying a start node (18), specifying one or more movement operations performable by the vehicle (2), and iteratively adding edges and further nodes (20-24) to the grid (16), each edge corresponding to a respective movement operation and each further node corresponding to a location for the vehicle (2). The one or more processors (12) then select a path through the grid (16) from a first node of the grid to a second node of the grid. The first node corresponds to a first location (A) for the vehicle (2) and the second node corresponds to a second location (B) for the vehicle (2). Thus, a route for the vehicle (2) from the first location (A) to the second location (B) is determined.