UAS Route Planning Using Social Media Risk Maps
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Solution Overview
Problem
Current regulations and risk assessment methods for Unmanned Aerial Systems (UAS) are inadequate for dynamic environments like urban areas, as they rely on static historical data and physics-based models that are computationally expensive and time-consuming, hindering the safe operation of UAS over populated areas.
Innovation Solution
A system using machine learning and artificial intelligence techniques, combining multilayer perceptrons and convolutional neural networks, to estimate ground risk by integrating social media data for population density and other spatial data, facilitating rapid risk assessment and dynamic route planning for UAS.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If physics-based models are used for risk assessment, then measurement precision is improved, but loss of time increases and productivity decreases
Solution Approach 1:
The system pre-computes risk maps for different locations and conditions before actual UAS flight operations. These pre-computed risk assessments are stored and can be quickly retrieved during route planning, eliminating the need for time-consuming real-time physics-based calculations while maintaining assessment accuracy
Solution Approach 2:
Instead of performing complex physics-based risk calculations in real-time, the system creates simplified representations (risk maps) that copy the essential risk characteristics of different areas. These risk maps can be rapidly processed and updated without repeating the full physics-based analysis, thus reducing computation time while preserving measurement precision
2Device complexity
If static historical data is used for risk assessment, then device complexity is reduced, but adaptability worsens
Solution Approach 1:
The system transitions from static historical data to dynamic real-time data collection using social media feeds, mobile device locations, and other contemporary sources. This allows the risk assessment system to adapt to changing population densities and events (such as concerts or protests) while maintaining manageable complexity through automated data processing pipelines
Solution Approach 2:
The system uses multi-functional data sources (social media platforms, mobile device networks, sensor data) that serve multiple purposes: population density estimation, event detection, and real-time risk assessment. This universal approach allows the system to adapt to various dynamic scenarios without requiring separate specialized systems for each function
3Productivity
If machine learning models are used for risk estimation, then productivity is improved, but device complexity increases
Solution Approach 1:
The machine learning system is divided into separate modular components: data collection modules from various sources, preprocessing modules for data cleaning and normalization, training modules for model development, and inference modules for real-time risk estimation. This segmentation allows each component to be developed and optimized independently, improving overall productivity while managing complexity through modular architecture
Data Source
AI summary
In some aspects, a method for determining a route by an unmanned aerial system (UAS) is described, the method including: receiving at least two locations, a first location and a second location; receiving UAS characteristics and spatial data; outputting a map based on the UAS characteristics and the spatial data, the map indicating a fatality rate; and outputting the route for the UAS to fly from the first location and the second location based on the map indicating the fatality rate. In some aspects, the spatial data can include at least one of: a social media activity; a density population; an area of one or more buildings; and a height of the one or more buildings. In some aspects, the route can be configured to be modified in response to at least one of safety or urgency.