Probabilistic Landing Area Map for Aircraft Path Planning
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Solution Overview
Problem
Current autonomous landing zone detection systems for aircraft, such as OPVs and UAVs, face challenges in accurately determining safe landing areas due to measurement uncertainty and the difficulty in selecting alternative landing areas, especially in complex environments with multiple path options.
Innovation Solution
A method and system that utilize a probabilistic safe landing area map, processed by a subsystem to generate and rank candidate landing areas based on constraints like mission-level and vehicle-level costs, geometric constraints, and confidence levels, with parallel processing and path planning to identify an optimal safe landing location.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If video camera-based vision systems are used to identify safe landing areas, then the system can detect landing zones, but the measurement precision and accuracy are insufficient due to uncertainty in identifying suitable areas
Solution Approach 1:
The system transforms the landing area identification from simple visual detection to a probabilistic parameter-based evaluation. By assigning probability scores to different landing areas based on multiple factors (terrain characteristics, accessibility, safety constraints), the system achieves more precise and reliable measurements of landing area suitability, directly resolving the accuracy and reliability contradiction.
Solution Approach 2:
The patent introduces a probabilistic safe landing area map as an intermediary data structure between the video camera input and the landing decision. This map aggregates multiple parameters and constraints to produce a refined probability distribution over potential landing areas, serving as a mediator that enhances measurement precision while maintaining system reliability.
2Adaptability or versatility
If multiple alternative landing areas are considered with different paths, then the system can find alternative landing zones, but the device complexity and computational requirements increase significantly
Solution Approach 1:
The system performs preliminary path planning for multiple candidate landing areas simultaneously using parallel processing. By pre-computing path costs and evaluating multiple options before final selection, the system achieves high adaptability in choosing alternative landing areas while managing computational complexity through organized parallel execution rather than sequential exploration.
Solution Approach 2:
The patent segments the path planning problem into independent parallel evaluations for each candidate landing area. Each processing resource handles a specific candidate's path analysis separately, allowing the system to maintain versatility in evaluating multiple alternatives while reducing overall complexity through modular, parallelized computation.
3Measurement precision
If probabilistic safe landing area maps with multiple constraints are processed, then the accuracy of optimal landing area determination improves, but the processing time and computational resources increase
Solution Approach 1:
The system pre-processes and filters candidate landing areas using constraints before performing detailed probabilistic evaluation. By eliminating obviously unsuitable areas early through constraint checking (geometric constraints, mission-level constraints), the system achieves high determination accuracy for the final selection while reducing the time spent analyzing only viable candidates.
Solution Approach 2:
The patent divides the processing into parallel segments where multiple processing resources simultaneously evaluate different candidate landing areas against the probabilistic map and constraints. This segmentation allows the system to maintain high determination accuracy through comprehensive constraint checking while reducing overall processing time through parallel execution of independent evaluations.
Data Source
AI summary
According to an aspect of the invention, a method of optimal safe landing area determination for an aircraft includes accessing a probabilistic safe landing area map that includes a plurality of probabilistic indicators of safe landing areas for the aircraft. A processing subsystem that includes one or more processing resources generates a list of candidate safe landing areas based on the probabilistic safe landing area map and one or more constraints. At least two of the candidate safe landing areas are provided to a path planner. The list of candidate safe landing areas is ranked based on results from the path planner indicating an estimated cost to reach each of the candidate safe landing areas. Based on the ranking, an indicator of an optimal safe landing area is output as a desired landing location for the aircraft.