Health-Aware Task Allocation for Multi-Agent Mission Continuity
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Solution Overview
Problem
Existing multi-agent autonomous systems lack real-time integration of prognostic health data for proactive re-tasking, leading to brittle and reactive behavior that can result in mission failure due to uncontrolled agent failures.
Innovation Solution
A system that integrates real-time prognostic health data through onboard sensors to estimate Remaining Useful Life (RUL) and transforms it into an Operational Risk Cost (Ω), which is used in a health-aware multi-objective optimization algorithm for dynamic task allocation, enabling proactive re-tasking to maintain mission continuity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional multi-agent task allocation algorithms optimize for performance metrics (mission time, energy consumption, operational cost), then productivity and efficiency are improved, but the system becomes brittle and vulnerable to unexpected failures due to accelerated component wear and lack of health awareness
Solution Approach 1:
The system implements a feedback mechanism where Remaining Useful Life (RUL) estimates from prognostic health management are continuously fed into the task allocation algorithm. This allows the system to adjust task assignments based on real-time agent health status, preventing over-stressing degraded agents and enabling proactive re-tasking before failures occur, thus resolving the contradiction between productivity optimization and system reliability
Solution Approach 2:
The task allocation system transitions from static optimization based solely on performance metrics to dynamic optimization that adapts to changing agent health conditions. The algorithm continuously re-evaluates task assignments as RUL estimates update, allowing the system to dynamically balance productivity goals with reliability constraints by adjusting which agents receive which tasks based on their current health state
2Device complexity
If the system operates under the assumption that all agents remain fully functional throughout the mission, then task allocation simplicity is maintained, but the system lacks proactive fault tolerance and must rely on reactive responses to catastrophic failures
Solution Approach 1:
The system performs preliminary actions by continuously monitoring agent health and estimating RUL before failures occur. This allows proactive re-tasking to be initiated while agents are still operational, preventing catastrophic failures rather than merely responding to them. The health-aware optimization algorithm proactively identifies at-risk agents and redistributes their tasks before failure, providing advance fault tolerance
Solution Approach 2:
The RUL estimate serves as an intermediary parameter that bridges the gap between simple task allocation and complex fault tolerance. By introducing this intermediate health metric into the allocation algorithm, the system gains proactive fault detection and response capabilities without requiring overly complex monitoring and control infrastructure, thus achieving improved reliability with manageable complexity
3Productivity
If optimization algorithms push agents to operational limits to maximize efficiency, then productivity is improved, but component wear accelerates and the probability of unforeseen failures increases
Solution Approach 1:
The system changes the optimization parameters from purely performance-based metrics to a composite that includes health-awareness and RUL estimates. This parameter transformation allows the optimization algorithm to identify task assignments that balance efficiency goals with component stress considerations, avoiding assignments that would push already-degraded agents to their limits and thereby reducing accelerated wear and unexpected failures
Data Source
AI summary
A system and method for fail-operational mission continuity in a multi-agent autonomous system. Each autonomous agent includes an onboard Prognostic Health Management (PHM) module that monitors health using sensor data. Upon detecting an incipient fault, the PHM module calculates a prognostic Remaining Useful Life (RUL). This RUL is transformed into a quantitative Operational Risk Cost (Ω) and communicated to a multi-agent control system. The control system's dynamic task allocation algorithm uses the Ω values as key inputs in a multi-objective optimization process. This enables the system to proactively and autonomously re-allocate a task from a degrading agent to a healthy agent before a failure occurs. The degrading agent is simultaneously commanded to perform a safe contingency maneuver. This integration of real-time prognostics and multi-agent control creates a resilient, self-healing system capable of completing missions despite hardware degradation.