Autonomous Multi-Pass UAV Data Acquisition Using 3D Mapping
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Solution Overview
Problem
Current UAV data acquisition methods require skilled operators and extensive processing, often resulting in inefficient data collection due to physical limitations of drones, such as limited maneuverability and flight times, especially in applications like agricultural surveys or infrastructure inspection, which demand high-resolution data over large areas.
Innovation Solution
The method involves a fleet of UAVs collecting initial coarse data to construct a 3D map, prioritizing areas for finer-grained data acquisition based on the map, and aggregating subsequent high-resolution data to form a composite view of the target area, leveraging advancements in UAV construction and processing power to optimize data collection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If UAVs collect high-resolution data over large areas, then measurement precision is improved, but productivity deteriorates due to limited flight times and maneuverability
Solution Approach 1:
The patent divides the data collection process into multiple passes: a first pass collecting coarse data at lower resolution, and a second pass collecting fine data at higher resolution. This segmentation allows the system to efficiently cover large areas in the first pass while obtaining high-resolution data only where needed in the second pass, thus resolving the contradiction between measurement precision and productivity
Solution Approach 2:
The system performs preliminary data collection in the first pass to identify areas of interest before conducting the second pass. By preliminarily mapping the target area and identifying regions requiring detailed examination, the system avoids wasting flight time and resources collecting high-resolution data across the entire large area, thereby improving productivity while maintaining measurement precision where necessary
2Reliability
If manual exploration is performed to ensure complete data coverage, then reliability is improved, but loss of time increases due to exhaustive processing requirements
Solution Approach 1:
The system uses feedback from the first pass data to guide the second pass data collection. The coarse data obtained in the first pass provides feedback about which areas require detailed examination, allowing the system to reliably identify areas of interest without requiring exhaustive manual exploration of the entire target area, thus reducing processing time while maintaining reliability
Solution Approach 2:
Instead of performing exhaustive data collection across the entire target area, the system applies partial action by focusing detailed high-resolution data collection only on identified areas of interest. This approach ensures reliable detection of anomalies in critical regions while avoiding the time-consuming process of exhaustive processing of the entire large area
3Measurement precision
If skilled operators are used to manage UAV data collection, then measurement precision is improved, but device complexity increases due to operational requirements
Solution Approach 1:
The system implements self-service by using automated algorithms to analyze the first pass data and automatically identify areas of interest for the second pass. This eliminates the need for skilled operators to manually interpret data and make decisions about where to focus detailed examination, thereby maintaining measurement precision while reducing operational complexity
Solution Approach 2:
The system automatically adjusts data collection parameters based on the analysis of first pass results. Instead of requiring skilled operators to manually configure UAV flight paths and data collection settings, the system dynamically changes parameters such as resolution and flight path based on identified areas of interest, maintaining data quality while reducing operational complexity
Data Source
AI summary
In one example, the present disclosure describes a device, computer-readable medium, and method for performing autonomous multi-pass data acquisition using unmanned aerial vehicle. For instance, in one example, a method includes obtaining a first set of sensor data collected by a fleet of unmanned aerial vehicles comprising at least one unmanned aerial vehicle, wherein the first set of sensor data depicts a target area at a first granularity, constructing a three-dimensional map of hierarchical unit representations of the first set of sensor data, sending a signal to the fleet of unmanned aerial vehicles to obtain a second set of sensor data at a second granularity that is finer than the first granularity, based at least in part on an examination of the three-dimensional map, and aggregating the second set of sensor data to form a high-resolution composite of the target area.