Aircraft Piloting Strategy Search Under Flight Constraints
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Solution Overview
Problem
Existing methods for generating aircraft trajectories are limited in their ability to address a wide range of air vehicles and constraints, often being specific to one type of aircraft or operating context, and fail to provide a comprehensive set of piloting strategies that optimize for various criteria such as fuel consumption or mimic specific avionics behaviors.
Innovation Solution
The use of Flight Intent Description Language (FIDL), Intent Composite Description Language (ICDL), and Aircraft Intent Description Language (AIDL) enables the creation of a decision tree that explores combinations of piloting strategies to comply with flight restrictions and optimization criteria, employing a greedy best-first search strategy to efficiently find optimal trajectories.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing methods for generating aircraft trajectories are used, then the trajectory generation process is simple, but the ability to address a wide range of air vehicles and constraints is limited
Solution Approach 1:
The patent implements a universal trajectory generation system that can handle multiple types of air vehicles (fixed-wing, rotary-wing, UAVs) and diverse constraints (air traffic management, weather, aircraft performance) through a single integrated framework. The system uses standardized data structures and constraint representations that can accommodate different vehicle types without requiring separate specialized systems for each.
Solution Approach 2:
The trajectory generation process is divided into distinct modular components: constraint definition module, objective function formulation module, optimization solver module, and validation module. Each module handles specific aspects of the problem independently, allowing the system to be extended to new vehicle types or constraints by adding or modifying individual modules rather than redesigning the entire system.
2Reliability
If comprehensive piloting strategies are generated to optimize for various criteria, then the optimization capability is improved, but the computational time increases
Solution Approach 1:
The system performs preliminary analysis and preprocessing of constraints and aircraft performance data before the main optimization process. By pre-processing the problem data and identifying key constraint interactions in advance, the system reduces the computational burden during the actual optimization phase, enabling comprehensive multi-criteria optimization without excessive computational time.
Solution Approach 2:
The optimization framework allows dynamic adjustment of parameter priorities and weights based on mission requirements. The system can switch between different optimization modes (e.g., fuel efficiency prioritization, time prioritization, or balanced optimization) by modifying parameter weights, enabling the same comprehensive strategy generation engine to adapt to different operational contexts with varying computational demands.
3Adaptability or versatility
If a decision tree exploring combinations of piloting strategies is created, then the comprehensiveness of strategies is improved, but the device complexity increases
Solution Approach 1:
The decision tree structure is implemented dynamically rather than statically. The tree is constructed and pruned on-the-fly during the optimization process based on the specific problem instance, constraint combinations, and search progress. This dynamic construction allows the system to explore comprehensive strategy combinations when needed while avoiding unnecessary complexity for simpler problems by limiting the tree depth and breadth adaptively.
Solution Approach 2:
The decision tree is organized with nested levels of abstraction, where higher-level nodes represent major strategic decisions and lower-level nodes represent specific maneuver parameters. This nested structure allows the system to manage complexity by making decisions at appropriate levels of granularity, exploring comprehensive strategies through systematic nesting of decision layers rather than exhaustive flat enumeration.
Data Source
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AI summary
Method for creating and choosing a determinate piloting strategy for flying an aircraft which comprises: generating an initial Flight context; expanding the initial Flight context thereby obtaining at least one expanded flight context; putting the initial Flight context and the at least one expanded flight context into a list of Flight contexts; selecting, from the list of Flight contexts, the flight context which is allocated a highest fitness value, and repeating the process described in steps "b" and "c" taking the selected Flight context as the initial flight context; wherein the method ends by choosing for a preferred piloting strategy for flying the aircraft a flight context, from the list of Flight contexts, whose allocated fitness value is the highest of all expanded contexts.