GNSS Risk Analysis Data for Autonomous Drone Routing
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Solution Overview
Problem
Autonomous vehicles and drones rely on reliable GPS signal coverage for safe navigation, but areas with obscured signals or multipath interference pose significant challenges, leading to potential malfunctions and safety risks.
Innovation Solution
The system generates and distributes risk analysis data based on historical satellite obscurations and environmental data, providing worst-case and best-case scenarios for GNSS signal availability, allowing for the identification of reliable routing paths.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If autonomous vehicles and drones operate in areas with obscured GNSS signals or multipath interference, then navigation coverage is expanded, but navigation reliability and safety deteriorate
Solution Approach 1:
The system performs preliminary risk analysis by collecting historical GNSS signal data, environmental data, and satellite orbital information before autonomous vehicles operate in potentially problematic areas. This advance preparation allows the system to identify and flag areas with potential signal issues before they affect navigation, resolving the contradiction by enabling expanded coverage while maintaining reliability through pre-assessment.
Solution Approach 2:
The system divides the operational area into multiple risk zones based on historical signal data and environmental factors. By segmenting the coverage area into high-risk, medium-risk, and low-risk zones, the system can provide differentiated navigation guidance and risk assessments, allowing expanded overall coverage while protecting reliability in critical segments through targeted risk management.
2Measurement precision
If risk analysis data is generated using extensive historical satellite path data and 3D environmental models, then accuracy of routing recommendations improves, but computational complexity and data processing requirements increase
Solution Approach 1:
The system pre-processes and stores historical GNSS signal data, environmental data, and satellite orbital information in structured formats before they are needed for routing decisions. This preliminary organization of data into searchable databases and pre-computed risk models reduces the computational burden during actual routing operations, maintaining high accuracy while managing complexity through advance preparation.
Solution Approach 2:
The system creates simplified representations and models of complex environmental data, such as 3D building models for ray-casting simulations and aggregated statistical models of signal behavior. These copied and simplified models retain the essential characteristics needed for accurate risk assessment while requiring significantly less computational resources than processing raw detailed data, thus resolving the accuracy-complexity contradiction.
3Reliability
If the system provides worst-case risk analysis for all possible flight conditions, then safety margin is maximized, but data volume and distribution bandwidth requirements increase
Solution Approach 1:
The system provides differentiated risk analysis by location, delivering detailed worst-case risk data only for areas where it is most critical (such as urban canyons, areas with tall structures, or locations with historical signal problems). For areas with consistently good signal conditions, the system provides summarized or reduced-risk assessments. This local quality approach maintains safety margins where needed while reducing overall data volume through targeted detail provision.
Solution Approach 2:
The system implements a tiered risk analysis approach where it provides full worst-case analysis for critical areas but uses simplified models or historical averages for less critical areas. This partial application of comprehensive analysis maintains adequate safety margins through selective detailed assessment while significantly reducing the total data volume that must be processed and distributed, resolving the contradiction between safety and data quantity.
Data Source
AI summary
Disclosed is route planning using a worst-case risk analysis and, if needed, a best-case risk analysis of GNSS coverage. The worst-case risk analysis identifies cuboids or 2d regions through which a vehicle can be routed with assurance that adequate GNSS coverage will be available regardless of the time of day that the vehicle travels. The best-case risk analysis identifies cuboids or 2d regions through which there is adequate coverage at some times during the day. In case path finding using the worst-case risk analysis fails, a best-case risk analysis can be requested and used to find alternate potential path(s). Time dependent forecast data that covers regions along the alternate potential path(s) can be requested and used to route vehicles, including autonomous drones, from starting points to destinations. This includes generation, distribution and use of risk analysis data, implemented as methods, systems and articles of manufacture.