UAV Flyable Airspace Validation Using Terrain Intersection Probability
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Solution Overview
Problem
Unmanned aerial vehicles (UAVs) face challenges in navigating through environments with obstacles, as existing methods rely on potentially inaccurate digital surface models (DSMs) for determining flyable airspaces.
Innovation Solution
A method is provided to generate candidate flyable airspaces by determining an occupancy grid of an area, simulating flight paths within this airspace, and calculating the probability of these paths intersecting occupied volumes. Based on this probability, the airspace is validated for UAV navigation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If digital surface models (DSMs) are used to determine flyable airspaces, then the process is simple and fast, but the accuracy and reliability of obstacle detection deteriorates due to potential inaccuracies in DSM data
Solution Approach 1:
The system performs preliminary simulation of multiple flight paths through candidate airspaces before final validation. By pre-evaluating probability values for intersecting occupied volumes through Monte Carlo simulations, the system prepares accurate safety assessments in advance, allowing fast final decisions without sacrificing reliability.
Solution Approach 2:
The system creates probabilistic occupancy grids that copy and represent the physical environment in digital space. Multiple simulated flight paths are generated as copies to evaluate different scenarios, allowing accurate probability assessment of obstacle intersections without requiring physical test flights.
2Measurement precision
If multiple flight paths are simulated to calculate probability values, then the accuracy of safety assessment improves, but the computational complexity and time required increases
Solution Approach 1:
The system simulates a sufficient number of flight paths (e.g., 1000 iterations) to achieve statistically meaningful probability values without performing exhaustive simulations. This partial action provides adequate accuracy for safety decisions while avoiding unnecessary computational overhead from excessive simulations.
Solution Approach 2:
The system changes the parameter of probability threshold values to balance accuracy and computational effort. By adjusting the threshold for validating flyable airspaces, the system can achieve acceptable safety assessment accuracy with fewer simulations, reducing computational complexity while maintaining sufficient measurement precision.
3Reliability
If a lower probability threshold is used for validating flyable airspaces, then the safety and reliability of UAV navigation improves, but the number of airspaces rejected as non-flyable increases, reducing operational efficiency
Solution Approach 1:
The system optimizes the probability threshold parameter to achieve the best balance between safety and operational efficiency. By carefully selecting threshold values based on risk tolerance and operational requirements, the system maintains high safety standards while minimizing the number of rejected airspaces that would reduce productivity.
Solution Approach 2:
The system uses feedback from simulated flight path results to continuously refine probability assessments and threshold selections. By analyzing patterns in simulation outcomes, the system can adjust validation criteria to maintain high safety reliability while improving operational efficiency through more accurate acceptance decisions.
Data Source
AI summary
A method comprises determining an occupancy grid of an area for unmanned aerial vehicle (UAV) navigation. The method further comprises determining, based on the occupancy grid, a candidate flyable airspace through which to permit UAV navigation and determining, by a UAV route planner, a plurality of UAV flight paths through the candidate flyable airspace. The method further comprises determining, based on the occupancy grid, a probability value of the plurality of UAV flight paths intersecting an occupied volume. The method additionally comprises determining whether the probability value of the plurality of UAV flight paths intersecting the occupied volume is below a threshold value and, based on determining that the probability value of the plurality of UAV flight paths intersecting the occupied volume is below the threshold value, validating the candidate flyable airspace for the UAV route planner to plan a route for a UAV to navigate through the area.