3D Flight Space Segmentation for Accurate Low-Load UAV Guidance
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Solution Overview
Problem
Current 3D space data generation methods for unmanned aerial vehicles (UAVs) face challenges in reducing data processing load while maintaining accurate flight guidance, leading to potential inaccuracies in navigation and operation efficiency in urban air mobility (UAM) environments.
Innovation Solution
A method and device for generating 3D space data by dividing the flight area and restricted areas into unit areas of varying sizes based on map data, including 3D geospatial information and obstacle information, with adjustments made to unit area sizes based on flight route, speed, direction, and vehicle size to optimize flight path generation and reduce data processing load.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If 3D space data is generated with high detail to maintain accurate flight guidance, then navigation accuracy is improved, but data processing load increases
Solution Approach 1:
The 3D space is divided into multiple unit areas (voxels) with varying sizes. Flight areas are divided into smaller units for accurate navigation, while restricted areas use larger units to reduce data volume. This segmentation allows the system to maintain high navigation accuracy in critical flight paths while reducing overall data processing requirements.
Solution Approach 2:
Different unit area sizes are applied to different spatial regions based on their functional requirements. Flight areas require finer granularity (smaller units) for precise guidance, whereas restricted areas can use coarser granularity (larger units). This local differentiation optimizes both navigation accuracy and data efficiency.
2Device complexity
If uniform unit area size is used throughout the 3D space, then data processing is simplified, but flight guidance accuracy near restricted areas deteriorates
Solution Approach 1:
The unit area size is made dynamic rather than static. The system automatically adjusts the size of units adjacent to restricted areas based on the proximity to flight routes. This dynamic adaptation ensures high guidance accuracy near critical boundaries while maintaining simpler processing in less critical regions.
Solution Approach 2:
The spatial resolution parameter (unit area size) is changed based on location and context. Units near restricted areas and flight routes use smaller sizes for accuracy, while units in open flight areas use larger sizes for efficiency. This parameter variation resolves the contradiction between uniformity and precision.
Data Source
AI summary
A 3D space data generation method for flight of an aerial vehicle may include receiving map data for a 3D space in which the aerial vehicle flies, and dividing a space into a flight area in which the aerial vehicle flies and a restricted area in which the aerial vehicle does not fly based on the map data and dividing the flight area and the restricted area into each unit area to generate 3D space data.