Dynamic Task Allocation for Rotary Aircraft Autonomy
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Solution Overview
Problem
Modern rotary aircraft pilots face high workload demands due to complexity, leading to potential flight errors during contingent situations like poor weather or threats, necessitating a system to aid pilots by dynamically allocating tasks between the crew and autonomous systems.
Innovation Solution
A vehicle autonomy management system allocates and adjusts task workloads between flight crews and flight-assist agents based on crew performance and selected autonomy levels, using situational awareness and performance sensors to determine task distribution and ensure timely responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If more flight tasks are allocated to the pilot to maintain full crew capability, then crew versatility is improved, but pilot workload and concentration requirements increase to demanding levels
Solution Approach 1:
The system dynamically adjusts the allocation of flight tasks between the pilot and flight-assist agents based on real-time monitoring of pilot workload and performance. The autonomy management system continuously evaluates pilot state and re分配s tasks to maintain optimal workload levels while ensuring mission objectives are met, transforming a static task allocation into a dynamic adaptive system.
Solution Approach 2:
Flight-assist agents serve as intermediaries between the pilot and aircraft systems, handling specific flight tasks and monitoring functions. These agents act as a buffer that reduces the direct cognitive load on the pilot while maintaining system capability, allowing the pilot to focus on high-level decision-making rather than routine monitoring tasks.
2Ease of operation
If autonomous systems take on more flight tasks to reduce pilot workload, then ease of operation is improved, but system complexity increases
Solution Approach 1:
The autonomous system is segmented into multiple independent flight-assist agents, each responsible for specific flight tasks such as navigation monitoring, system management, or threat detection. This modular architecture reduces overall system complexity by allowing independent development, testing, and management of individual agent functions while maintaining collective capability.
Solution Approach 2:
The flight-assist agents are designed with multi-functionality to perform various flight tasks across different operational scenarios. A single agent architecture can adapt to handle navigation, monitoring, communication, and emergency response functions, reducing the need for separate specialized systems and thereby managing complexity while providing comprehensive assistance.
3Reliability
If the system dynamically adjusts task allocation based on pilot performance, then flight safety is improved, but measurement and monitoring requirements increase
Solution Approach 1:
The autonomy management system implements continuous feedback loops that monitor pilot workload indicators, response times, and performance metrics. This feedback is used to dynamically adjust task allocation in real-time, transferring tasks to flight-assist agents when pilot workload exceeds thresholds and returning tasks when pilot capacity increases, thereby maintaining optimal safety margins through adaptive response to measured performance data.
Data Source
AI summary
A system and method for flying an aircraft is disclosed. The system includes one or more flight-assist agents for performing an operation related to flying the aircraft and a vehicle autonomy management system. The vehicle autonomy management system allocates tasks of a task workload involved in the operation between a flight crew and the one or more flight-assist agents, monitors a performance of the flight crew in executing a portion of the task workload allocated to the flight crew, and adjusts an allocation of the task workload between the flight crew and the one or more flight-assist agents based on the performance of the flight crew.