UAV Flight Risk Modeling for Concurrent Route Scheduling
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Solution Overview
Problem
Managing the safe operation of a fleet of aerial vehicles, particularly in concurrent delivery missions, is challenging due to the need for effective scheduling and route planning in dynamic geographic environments, where existing technologies fail to adequately assess and mitigate risks such as collisions and human harm.
Innovation Solution
A risk-based model is developed to determine and manage flight planning and operations for unmanned aerial vehicles (UAVs), incorporating path risks associated with individual flights and concurrence risks from simultaneous operations, using aggregate path and concurrence risk calculations to optimize flight paths and scheduling to minimize total risk.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multiple aerial vehicles operate concurrently in a geographic area, then productivity increases, but the risk of collision and harmful factors increases
Solution Approach 1:
The system performs preliminary risk assessment and flight path optimization before concurrent operations begin. The risk management system calculates aggregate path risks and concurrence risks in advance, adjusts flight paths and scheduling proactively, and eliminates high-risk missions before they are executed, preventing collisions rather than reacting to them
Solution Approach 2:
The risk management system acts as an intermediary between fleet operations and safety outcomes. It introduces a computational layer that calculates risks, adjusts parameters, and mediates between the desire for high productivity and the need for safety by optimizing flight paths and scheduling based on real-time risk assessments
2Reliability
If flight paths are adjusted to reduce risk, then safety improves, but operational time and complexity increase
Solution Approach 1:
The system dynamically adjusts flight paths and scheduling based on real-time risk assessments rather than using static pre-planned routes. The risk management system continuously monitors concurrence risks and modifies operational parameters on-the-fly, allowing adaptive optimization that balances safety requirements with time efficiency
Solution Approach 2:
The system changes operational parameters such as flight path coordinates, altitude, speed, and scheduling times based on calculated risk levels. By systematically adjusting these parameters to minimize aggregate risk while maintaining operational efficiency, the system achieves safety improvements without excessive time loss
3Reliability
If comprehensive risk assessment is performed for all concurrent operations, then safety improves, but computational complexity and processing time increase
Solution Approach 1:
The risk assessment process is segmented into distinct computational components: path risk calculation for individual flights, concurrence risk calculation for pairs of flights, and aggregate risk summation. This modular approach allows the system to manage complexity by breaking down the comprehensive assessment into manageable segments that can be processed systematically
Data Source
AI summary
Described are example systems and methods for facilitating management of flight planning and operations for unmanned vehicles, such as unmanned aerial vehicles (UAVs). The described systems and methods can facilitate generation of a risk-based model that incorporates the risk presented by certain flight paths as well as the risk of collisions between concurrently operating vehicles. The described systems and methods can also facilitate determining a lowest relative total risk for planned concurrent operations. The total risk can also be compared against a risk threshold to determine whether the total risk associated with the planned operations is within an acceptable range.