AI Flight Trajectory Control for Collaborative Attritable Aircraft
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Solution Overview
Problem
Modern aircraft require extensive training for pilots to make quick and effective decisions under dynamic conditions, necessitating a solution that shares the decision-making burden and enhances pilot performance.
Innovation Solution
A collaborative multi-agent artificial intelligence (AI) control system integrating large maneuvering and language neural networks to receive and process data, generate flight trajectories, and assist pilots with maneuvering, tactics, and procedures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If pilots receive extensive training to operate modern aircraft, then their decision-making capability improves, but the time required for training increases
Solution Approach 1:
An AI system acts as an intermediary between the pilot and the complex aircraft systems, providing real-time decision support by processing sensor data, evaluating tactical options, and presenting recommended actions. This mediator handles the complexity of modern aircraft operations, allowing pilots to make effective decisions without requiring exhaustive training on all system interactions.
Solution Approach 2:
The aircraft system performs self-service by automatically monitoring its own state, detecting threats, and generating tactical recommendations without requiring constant pilot intervention or deep pilot knowledge of all system parameters. The system serves itself by maintaining situational awareness and presenting processed information to the pilot.
2Productivity
If pilots make consequential decisions under dynamic conditions, then operational effectiveness improves, but the cognitive load on pilots increases
Solution Approach 1:
The decision-making process is segmented into distinct functional modules: threat detection, tactical evaluation, option generation, and recommendation presentation. Each module handles a specific aspect of the decision process, reducing the cognitive load on the pilot by presenting information in organized, manageable segments rather than requiring simultaneous processing of all factors.
Solution Approach 2:
The AI system serves as a cognitive intermediary that handles the heavy lifting of analyzing dynamic conditions and evaluating tactical options, freeing the pilot from excessive cognitive load while maintaining operational effectiveness. The system translates complex sensor data and tactical considerations into actionable recommendations.
3Extent of automation
If aircraft systems integrate multiple neural network models for maneuvering and tactics, then autonomous operation capability improves, but system complexity increases
Solution Approach 1:
The autonomous operation system is segmented into specialized neural network models, each handling specific functions such as maneuvering control, tactical decision-making, and threat response. This modular architecture allows the system to achieve high autonomous capability while managing complexity through functional separation, where each model can be developed and validated independently.
Solution Approach 2:
Multiple specialized neural network models are merged into a unified autonomous operation system that integrates maneuvering control, tactical awareness, and decision-making capabilities. The merging of these models creates a cohesive autonomous system that leverages the strengths of each individual model while presenting a unified interface for control.
Data Source
AI summary
A collaborative multi-agent artificial intelligence (AI) control system configured to operatively integrate with a selectively-autonomous, selectively-collaborative low-cost attritable aircraft (SA-SC-LCAA) is disclosed. The AI control system generally comprises one or more large maneuvering neural network models and one or more a large language neural network models that work together collaboratively, including neural network models configured to: receive pilot speech, attention, and biometric data, as well as aircraft switchology and control system actuation data from the low-cost attritable aircraft; receive aircraft operational data and time-space-position-information (TSPI) from the low-cost attritable aircraft; receive TSPI data for friendly aircraft, neutral aircraft, and or threat aircraft; generate at least one candidate aircraft flight trajectory/ies to fly a selected tactic that includes techniques and procedures (TTP); select one trajectory from the at least one candidate aircraft flight trajectory/ies to fly the aircraft to fly a selected TTP; and operate the low-cost attritable aircraft in accordance with the selected trajectory and TTP. In the preferred embodiment, the neural network model comprises a maneuvering, tactics, techniques, and procedures large language model (MTTP-LMM). The selected flight trajectory comprises a sequence of one or more planned maneuvers for the low-cost attritable aircraft to complete a selected TTP. The selected TTP can be displayed graphically along with the flight trajectories of other aircraft in proximity to the low-cost attritable aircraft. A physical model and energy-maneuverability model of the low-cost attritable aircraft may be employed to generate the at least one candidate aircraft flight trajectory and TTP.