UAV Detect-and-Avoid Using VHF Speech for Dynamic Obstacle Intent
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Solution Overview
Problem
Unmanned aerial vehicles (UAVs) face challenges in detecting and avoiding dynamic obstacles due to incomplete knowledge graphs, as current systems like ADS-B do not provide flightpath or pilot intent information for all aircraft, limiting their ability to navigate safely in shared airspace.
Innovation Solution
Integrating speech recognition technology to analyze VHF radio communications, supplementing the knowledge graph with information on dynamic obstacles such as aircraft position, velocity, flightpath, and pilot intent, enhancing the UAV's detect and avoid (DAA) system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If speech recognition technology is integrated to analyze VHF radio communications, then the completeness of the knowledge graph is improved, but the device complexity increases
Solution Approach 1:
The patent introduces speech recognition technology as an intermediary component that processes VHF radio communications and extracts relevant flightpath and pilot intent information. This intermediary system bridges the gap between raw radio signals and the knowledge graph, converting unstructured audio data into structured information without requiring direct modification of the core navigation system.
Solution Approach 2:
The speech recognition system serves multiple functions: it transcribes VHF radio communications, identifies pilot intent, extracts flightpath information, and updates the knowledge graph. By consolidating these diverse functions into a single multi-functional module, the system reduces overall complexity while improving information completeness.
2Loss of information
If speech recognition is used to analyze VHF radio communications, then pilot intent information is obtained, but processing time increases
Solution Approach 1:
The system performs preliminary speech recognition processing on VHF radio communications to extract pilot intent information before critical navigation decisions are required. By advance-processing the audio data and pre-populating the knowledge graph with intent information, the system reduces real-time processing requirements during actual navigation and obstacle avoidance maneuvers.
3Measurement precision
If comprehensive speech analysis is implemented, then detection accuracy is improved, but energy consumption increases
Solution Approach 1:
The speech recognition system implements partial analysis by focusing only on the most critical aspects of VHF radio communications - specifically pilot intent and flightpath information - rather than attempting to transcribe and analyze every detail of the communication. This selective approach maintains sufficient detection accuracy while significantly reducing computational load and energy consumption compared to comprehensive speech analysis.
Data Source
AI summary
A technique for detecting and avoiding obstacles by an unmanned aerial vehicle (UAV) includes: querying a knowledge graph having information related to a dynamic obstacle that may be in proximity to the UAV when traveling along a planned route; comparing the location of the dynamic obstacle to the UAV to detect conflicts; and in response to detecting a conflict, performing an action to avoid conflict with the dynamic obstacle. The knowledge graph can be updated by receiving a VHF radio signal containing the information related to the dynamic obstacle in the audible speech format; translating the audible speech format to a text format using speech recognition; analyzing the text format for relevant information related to the dynamic obstacle; comparing the relevant information related to the dynamic obstacle of the text format to the knowledge graph to detect changes; and updating the knowledge graph.