UAV Overlapping Coalition Allocation for Multi-Task Cooperation
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Solution Overview
Problem
Existing UAV task cooperation methods based on coalition formation games are limited, as they primarily focus on single-task execution, ignore task execution costs, and do not account for the diverse capabilities of modern UAVs, which restrict resource allocation flexibility and overall task execution utility.
Innovation Solution
A UAV task cooperation method using an overlapping coalition formation game model with a sequential task execution mechanism, incorporating a bilateral mutual benefit transfer order and a preference gravity-guided Tabu Search algorithm to optimize resource allocation and improve task execution utility, allowing UAVs to allocate resources flexibly across multiple tasks while considering costs and diverse capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If non-overlapping coalition formation game model is used, then task allocation is simplified, but resource allocation flexibility is reduced
Solution Approach 1:
The patent segments the coalition formation process into two independent stages: (1) UAVs form coalitions based on spatial proximity and task requirements, and (2) Resources are dynamically allocated within and across coalitions. This segmentation allows simplified coalition formation while maintaining flexible resource allocation through the resource allocation module that considers multiple factors including UAV capabilities, task requirements, and coalition composition.
Solution Approach 2:
The patent introduces a new dimension of resource allocation that operates independently from coalition formation. While traditional approaches only consider coalition membership, this patent adds a resource allocation dimension that evaluates UAV capabilities, task requirements, energy consumption, and payload constraints, enabling flexible resource distribution across overlapping coalitions without increasing formation complexity.
2Reliability
If single-task execution mode is adopted, then task completion focus is improved, but overall task execution utility is reduced
Solution Approach 1:
The patent implements a dual-mode execution mechanism where UAV coalitions can dynamically switch between single-task execution mode (for high reliability on critical tasks) and multi-task execution mode (for improved overall productivity). The resource allocation module evaluates task priorities, UAV capabilities, and coalition composition to determine the appropriate execution mode, enabling the system to achieve both reliability and productivity.
Solution Approach 2:
The patent introduces dynamic task execution scheduling where the execution mode (single-task or multi-task) is not fixed but adapts based on real-time conditions including task urgency, resource availability, and coalition performance. This dynamic approach allows the system to prioritize reliable single-task execution when needed while utilizing multi-task execution to maximize overall productivity when conditions permit.
3Ease of operation
If task execution costs are ignored, then allocation decision simplicity is improved, but resource allocation optimality is reduced
Solution Approach 1:
The patent performs preliminary cost estimation and evaluation during the resource allocation planning phase. The resource allocation module pre-calculates energy consumption, payload constraints, and task completion costs for different allocation scenarios before making final decisions. This preliminary action provides cost-aware guidance to the allocation process without requiring complex real-time cost calculations, maintaining decision simplicity while improving optimality.
4Device complexity
If diverse UAV capabilities are not distinguished, then resource allocation simplicity is improved, but task matching precision is reduced
Solution Approach 1:
The patent implements local quality differentiation where each UAV's capabilities are evaluated and matched to specific task requirements based on local characteristics. The resource allocation module considers individual UAV properties such as energy capacity, payload weight, sensor types, and communication capabilities, matching these local qualities to corresponding task demands. This approach achieves precise task-resource matching without requiring complex system-wide capability differentiation.
Data Source
AI summary
An unmanned aerial vehicle (UAV) task cooperation method based on an overlapping coalition formation (OCF) game includes: constructing a sequential OCF game model for a UAV multi-task cooperation problem; using a bilateral mutual benefit transfer (BMBT) order that is biased toward the utility of a whole coalition to evaluate a preference of a UAV for a coalitional structure; optimizing task resource allocation of the UAV under an overlapping coalitional structure by using a preference gravity-guided Tabu Search algorithm to form a stable coalitional structure; and optimizing a transmission strategy based on the current coalitional structure, an updated status of a task resource allocation scheme of the UAV, and a current fading environment, so as to maximize task execution utility of a UAV network. The method quantifies characteristics of resource properties of the UAV and a task, and optimizes the task resource allocation of the UAV under the overlapping coalitional structure.