UAV Fleet Resource Allocation Using Competition Network Graphs
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Solution Overview
Problem
The allocation of resources to autonomous vehicle missions is inefficient due to large cardinalities, leading to unbounded service times and mission wait times, which existing methods struggle to optimize in a scalable and timely manner.
Innovation Solution
A resource management system using a resource competition network graph (RCN graph) to model conflicts and optimize resource allocation, employing optimization techniques on mutated subgraphs to decouple non-competing missions, ensuring efficient allocation even with a large number of resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If exhaustive enumeration of all possible allocations is performed to optimize resource allocation, then optimal utility is achieved, but computational cost becomes prohibitively expensive
Solution Approach 1:
The patent segments the large-scale resource allocation problem into smaller subproblems by introducing abstraction layers (mission-level, candidate-level, resource-level) and using graph-based decomposition. The RCN graph divides the problem into manageable components that can be processed independently or in parallel, avoiding the need to enumerate all possible allocations at once.
Solution Approach 2:
The patent introduces intermediate data structures and algorithms as mediators between the raw resource allocation problem and the final solution. The RCN graph, candidate records, and optimization algorithms serve as intermediaries that transform the complex exhaustive search into a tractable optimization problem, reducing computational complexity while preserving optimality.
2Productivity
If resource allocation is optimized to maximize throughput, then delivery capacity increases, but service times may become unbounded when utilization factor exceeds 1
Solution Approach 1:
The patent implements feedback mechanisms by continuously monitoring utilization factors and adjusting resource allocation decisions accordingly. The system uses real-time data about mission arrivals, resource availability, and current allocations to dynamically optimize assignments, ensuring that throughput is maximized while service times remain bounded through adaptive control.
Solution Approach 2:
The patent employs dynamic resource allocation where the allocation strategy adapts to changing system conditions. Rather than static assignments, the system continuously re-evaluates and re-optimizes resource allocations based on current utilization factors, mission priorities, and resource availability, allowing the system to maintain performance across varying load conditions.
3Quantity of substance
If the number of UAVs and resources is increased to meet peak demand, then mission intake capacity increases, but resource allocation complexity and cost increase
Solution Approach 1:
The patent creates universal resource models and candidate records that can represent multiple types of resources (UAVs, landing pads, facilities) in a unified framework. This multi-functional approach allows the same allocation algorithms to handle diverse resource types without increasing complexity, as the RCN graph and optimization methods work uniformly across different resource categories.
Solution Approach 2:
The patent manages complexity by changing parameters through abstraction and aggregation. Instead of tracking individual resource details, the system uses aggregated metrics, utilization factors, and graph-based representations that capture essential relationships without the full complexity of individual resource characteristics, enabling scalable allocation as resource numbers increase.
Data Source
AI summary
In some embodiments, a computer-implemented method for managing resources of a fleet of unmanned aerial vehicles (UAVs) is provided. A computing system creates a mission record and one or more candidate records. Each candidate record of the one or more candidate records represents one or more resources for accomplishing a mission represented by the mission record. The computing system adds a mission node representing the mission record to a resource competition network graph (RCN graph). The computing system adds one or more candidate nodes representing the one or more candidate records to the RCN graph. The computing system determines an optimized allocation of candidate records to mission records using at least a subgraph of the RCN graph. A candidate record is determined to commit to a mission record, and the computing system updates the RCN graph to commit the candidate record to the mission record.