Occupancy Map Refinement for Urban UAV Route Planning
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Solution Overview
Problem
Current route planning systems for aerial vehicles, particularly unmanned aerial vehicles (UAVs), face inefficiencies when navigating through densely populated or complex urban areas, as existing methods often require extensive computational resources and do not adequately consider the sizes and shapes of obstacles, leading to suboptimal route selection.
Innovation Solution
The system divides a region into a grid of cells based on occupancy data, calculating occupancy metrics to label cells as obstructed or free, and further subdivides cells until they align with the vehicle's operational capabilities, creating an occupancy map that aids in selecting safe and efficient routes using algorithms like A* search.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the region is divided into a fine grid to accurately represent obstacles and occupancy, then the precision of route planning improves, but the computational complexity increases significantly
Solution Approach 1:
The patent divides the operational space into a grid of cells and further segments cells with intermediate occupancy metrics into subcells. This hierarchical segmentation allows the system to maintain high precision where needed (at obstacle boundaries) while using coarser resolution in free spaces, thereby reducing overall computational complexity while preserving measurement precision for route planning decisions.
Solution Approach 2:
The patent applies different levels of grid refinement locally based on occupancy characteristics. Cells containing obstacles or intermediate occupancy metrics are subdivided into smaller subcells with higher precision, while cells clearly identified as free require no further subdivision. This local quality approach ensures high measurement precision at critical locations while minimizing computational complexity in non-critical areas.
2Reliability
If cells are subdivided to align with vehicle operational capabilities, then the reliability of route selection improves, but the time required for route planning increases
Solution Approach 1:
The patent performs preliminary subdivision of cells into subcells based on occupancy metrics before actual route planning begins. By pre-processing the spatial data and identifying which cells require further subdivision, the system prepares the operational space in advance, allowing the route planning algorithm to operate on a pre-organized structure that reduces computation time while maintaining reliability.
Solution Approach 2:
The patent dynamically adjusts the level of cell subdivision based on vehicle operational capabilities and occupancy characteristics. The system determines the appropriate subcell size relative to vehicle dimensions and operational parameters, creating a adaptive resolution that matches the specific mission requirements. This dynamic approach ensures reliability without unnecessarily increasing planning time for all scenarios.
3Reliability
If the system considers detailed obstacle sizes and shapes in route planning, then the safety of the route improves, but the computational resources required increase
Solution Approach 1:
The patent extracts and isolates the critical geometric information needed for safety assessment from the full obstacle data. By representing obstacles through occupancy metrics in grid cells and subcells rather than processing complete obstacle geometries, the system extracts only the essential spatial information required for safe route planning, significantly reducing computational energy requirements while maintaining flight safety.
Data Source
AI summary
An occupancy map of a region is generated for a vehicle by dividing the region into cells and calculating occupancy metrics for each of the cells based on building footprints or other data. Cells having high occupancy metrics are labeled as obstructed, and cells having low occupancy metrics are labeled as free of obstructions. Cells that may not be labeled as obstructed or free based on their occupancy metrics are subdivided, and occupancy metrics are calculated for cells formed from the subdivision. Occupancy metrics may be calculated for cells, and the cells may be subdivided, for as long as the cells formed from the subdivision have areas greater than an area defined based on operational capabilities of the vehicle. The occupancy map is used to determine routes through the region for the vehicle.