UAV Charging Pad Mapping With Fiducial Markers for Accurate Docking
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Solution Overview
Problem
Existing uncrewed aerial vehicles (UAVs) face challenges in accurately navigating to and identifying charging pads in clusters without precise geolocation data, leading to potential misidentification and inefficiencies in battery charging.
Innovation Solution
The use of fiducial markers in a cluster layout, combined with context-based navigation techniques, allows UAVs to disambiguate charging pads by recognizing patterns and orientations, enabling accurate navigation without requiring precise geolocation measurements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If UAVs navigate to charging pads using traditional GPS-based geolocation data, then navigation can be performed with existing infrastructure, but accuracy deteriorates in clusters where multiple charging pads share similar geocoordinates
Solution Approach 1:
The patent introduces fiducial markers as intermediary objects between the UAV's navigation system and the charging pads. These markers serve as mediators that provide distinctive visual identifiers and spatial relationship data, enabling the UAV to disambiguate between multiple charging pads that share similar GPS coordinates. The markers act as an intermediate reference layer that bridges the gap between coarse geolocation data and precise pad identification.
Solution Approach 2:
The patent transitions from relying solely on two-dimensional GPS coordinates to incorporating visual dimension data through fiducial markers. By adding the visual recognition dimension (marker patterns, orientations, and relative positions), the system gains an additional layer of information that resolves ambiguities in the geolocation dimension alone. This dimensional enrichment allows the UAV to distinguish between charging pads that would otherwise be indistinguishable using GPS alone.
2Measurement precision
If fiducial markers are deployed at precise geolocations to enable accurate UAV navigation, then navigation accuracy improves, but the complexity of infrastructure deployment and maintenance increases
Solution Approach 1:
The patent changes the parameter of marker placement precision from requiring exact geolocation coordinates to requiring only consistent relative positioning within the cluster. The fiducial markers need not be placed at precisely surveyed locations; instead, their value comes from their stable relative positions to one another and to the charging pads. This parameter relaxation significantly reduces deployment complexity while maintaining navigation accuracy through the optimization process.
Solution Approach 2:
The system employs self-service mechanisms where the UAVs themselves contribute to maintaining the navigation infrastructure. By capturing images of the fiducial markers and reporting their observed positions, the UAVs automatically provide feedback that enables the optimization algorithm to update and maintain accurate spatial relationships. This eliminates the need for manual surveying and re-surveying operations, allowing the system to self-correct and maintain accuracy over time.
3Reliability
If routine aerial images are captured and processed to maintain accurate maps of charging pad clusters, then map accuracy is maintained for navigation, but computational resources and processing time are consumed
Solution Approach 1:
The patent applies partial action by selectively processing only the necessary image data for map maintenance. Rather than performing exhaustive analysis of all image content, the system focuses specifically on detecting and measuring the fiducial markers, which are the critical elements for navigation accuracy. This selective processing approach reduces computational overhead while maintaining the reliability needed for safe UAV operations.
Solution Approach 2:
The system performs preliminary action by establishing the fiducial marker infrastructure and initial spatial relationships before operational UAV navigation begins. The optimization algorithm pre-processes the spatial data and creates the reference map framework in advance, so that during actual operations, the system only needs to perform incremental updates rather than complete re-processing. This preliminary setup reduces the time burden during routine maintenance operations.
Data Source
AI summary
A computing system in an infrastructure support network for uncrewed aerial vehicles (UAVs) may receive, from a UAV, aerial observation data of a ground-based cluster of charging pads for UAVs, the cluster comprising assets including the charging pads arranged in a layout and fiducial markers distributed across the layout. The aerial observation data may comprise position measurements of the UAV at aerial geolocations above the cluster, and vector positions of one or more assets with respect to the aerial geolocations. The computing system may generate a map graph from the aerial observation data, the map graph comprising (i) nodes corresponding to both the aerial geolocations and vector positions, and (ii) edges between pairs of selected nodes, the edges corresponding to distances between selected nodes and including measurement uncertainties. The computing system may generate a spatial map of cluster assets of the cluster by computationally optimizing the map graph.