UAV Tactical Deconfliction via Dynamic Distance Parameters
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Solution Overview
Problem
Current air traffic management systems are inadequate for navigating multiple unmanned aerial vehicles (UAVs) in complex Beyond Visual Line of Sight (BVLOS) scenarios, leading to restricted flights and impeded industry growth, as they lack a dedicated automated tactical deconfliction system capable of continuous deconfliction in shared airspaces.
Innovation Solution
A computer-implemented method and system that obtain flight and environmental data to determine potential conflicts, calculate a minimum distance parameter based on UAV types and data quality, and compute alternative flight paths for UAVs, enabling automatic tactical deconfliction maneuvers without additional hardware, using software-to-software communication through Unmanned Traffic Management (UTM) or UAV fleet management systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing air traffic management systems are used for UAV navigation, then system simplicity is maintained, but collision avoidance capability in complex BVLOS scenarios deteriorates
Solution Approach 1:
The system segments the complex deconfliction task into distinct functional modules: a conflict detection module that identifies potential collisions, a decision module that determines avoidance maneuvers, and a control module that executes flight path adjustments. This modular architecture enables reliable collision avoidance while managing system complexity through organized functionality.
Solution Approach 2:
The system performs preliminary conflict detection and avoidance planning before actual collisions occur. By continuously monitoring flight paths and predicting potential intersections, the system proactively initiates deconfliction maneuvers, ensuring reliable collision avoidance rather than reacting after problems arise.
2Reliability
If automated tactical deconfliction system is implemented, then collision prevention capability is improved, but system complexity increases
Solution Approach 1:
The automated tactical deconfliction system is designed to handle multiple UAV types, various conflict scenarios, and different flight conditions through a universal algorithm framework. This multi-functionality approach improves deconfliction effectiveness across diverse situations while avoiding the need for separate specialized systems for each scenario.
Solution Approach 2:
The system enables UAVs to autonomously detect conflicts, calculate avoidance maneuvers, and execute deconfliction without continuous human intervention. This self-service capability improves reliability by ensuring continuous monitoring and immediate response, while the automation actually reduces the operational complexity burden on human operators.
3Reliability
If continuous deconfliction monitoring is performed for multiple UAVs, then safety is improved, but computational load increases
Solution Approach 1:
The system applies partial monitoring intensity based on situational context: in low-risk scenarios with well-separated flight paths, monitoring is reduced to essential checks, while in high-risk situations with potential conflicts, full continuous monitoring is activated. This approach maintains flight safety while optimizing computational energy consumption by avoiding excessive processing in safe conditions.
Solution Approach 2:
The system performs conflict detection and deconfliction calculations at periodic intervals rather than continuously, updating flight path assessments at predetermined time steps or distance milestones. This periodic approach ensures adequate safety monitoring while significantly reducing computational energy requirements compared to truly continuous real-time processing.
4Measurement precision
If minimum distance parameter is dynamically calculated based on data quality, then deconfliction accuracy is improved, but processing complexity increases
Solution Approach 1:
The system dynamically adjusts the minimum distance parameter based on measured data quality metrics such as sensor accuracy, signal strength, and environmental conditions. When data quality is high, smaller safety margins are used; when data quality degrades, larger margins are applied. This parameter adaptation improves deconfliction accuracy by matching safety distances to actual measurement reliability while using straightforward calculation logic.
Data Source
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Figure 6A~6D
AI summary
A method and system for tactical deconfliction of aerial vehicles based on detecting possible conflicts (5) between aerial vehicles and additional objects (4) in a deconfliction space (2); determining a minimum distance parameter (6) for interfering aerial vehicles in a possible conflict (5) based on the interfering types of aerial vehicles and data quality (10) of the obtained flight data (8) and the environmental data (9) describing the additional objects (4); and calculating an alternative flight path (7) for interfering aerial vehicles based on the minimum distance parameter (6). The alternative flight path calculation can further take into account priority levels (23) assigned to the aerial vehicles, and can be optimized for a global variable (17) based on unit variables (18) derived from calculated flight path deviations of each individual aerial vehicle.