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A Task-Driven Multi-UAV Coalition Formation Mechanism

Xinpeng Lu, Heng Song, Huailing Ma, Junwu Zhu

TL;DR

An algorithm for coalition formation based on marginal utility was proposed, which utilized Shapley value to achieve fair utility distribution within the coalition, evaluated coalition values based on marginal utility preference order, and achieved stable coalition partition through a limited number of iterations.

Abstract

With the rapid advancement of UAV technology, the problem of UAV coalition formation has become a hotspot. Therefore, designing task-driven multi-UAV coalition formation mechanism has become a challenging problem. However, existing coalition formation mechanisms suffer from low relevance between UAVs and task requirements, resulting in overall low coalition utility and unstable coalition structures. To address these problems, this paper proposed a novel multi-UAV coalition network collaborative task completion model, considering both coalition work capacity and task-requirement relationships. This model stimulated the formation of coalitions that match task requirements by using a revenue function based on the coalition's revenue threshold. Subsequently, an algorithm for coalition formation based on marginal utility was proposed. Specifically, the algorithm utilized Shapley value to achieve fair utility distribution within the coalition, evaluated coalition values based on marginal utility preference order, and achieved stable coalition partition through a limited number of iterations. Additionally, we theoretically proved that this algorithm has Nash equilibrium solution. Finally, experimental results demonstrated that the proposed algorithm, compared to currently classical algorithms, not only forms more stable coalitions but also further enhances the overall utility of coalitions effectively.

A Task-Driven Multi-UAV Coalition Formation Mechanism

TL;DR

An algorithm for coalition formation based on marginal utility was proposed, which utilized Shapley value to achieve fair utility distribution within the coalition, evaluated coalition values based on marginal utility preference order, and achieved stable coalition partition through a limited number of iterations.

Abstract

With the rapid advancement of UAV technology, the problem of UAV coalition formation has become a hotspot. Therefore, designing task-driven multi-UAV coalition formation mechanism has become a challenging problem. However, existing coalition formation mechanisms suffer from low relevance between UAVs and task requirements, resulting in overall low coalition utility and unstable coalition structures. To address these problems, this paper proposed a novel multi-UAV coalition network collaborative task completion model, considering both coalition work capacity and task-requirement relationships. This model stimulated the formation of coalitions that match task requirements by using a revenue function based on the coalition's revenue threshold. Subsequently, an algorithm for coalition formation based on marginal utility was proposed. Specifically, the algorithm utilized Shapley value to achieve fair utility distribution within the coalition, evaluated coalition values based on marginal utility preference order, and achieved stable coalition partition through a limited number of iterations. Additionally, we theoretically proved that this algorithm has Nash equilibrium solution. Finally, experimental results demonstrated that the proposed algorithm, compared to currently classical algorithms, not only forms more stable coalitions but also further enhances the overall utility of coalitions effectively.
Paper Structure (19 sections, 4 theorems, 5 equations, 6 figures, 1 table, 3 algorithms)

This paper contains 19 sections, 4 theorems, 5 equations, 6 figures, 1 table, 3 algorithms.

Table of Contents

  1. Introduction
  2. Related Work
  3. Coalition Networks in Multi-UAV
  4. Coalition Formation Games
  5. Problem Definition
  6. System Model
  7. Proof of Theorem
  8. Methodology
  9. Basic Concepts
  10. Multi-UAV Coalition Formation Algorithm Based on Marginal Utility
  11. A Coalition Utility Allocation Algorithm Based on Shapley Values
  12. A Coalition Value Evaluation Algorithm Based on Marginal Utility Order
  13. Convergence Proof
  14. Experimental Results
  15. Experimental Setup
  16. ...and 4 more sections

Key Result

theorem 1

If the coalition's utility function $V_i(C_i)$ and the task revenue function $R_i(e^i)$ exhibit the same monotonicity, then $2p_i/(1+\sqrt{1+\frac{4V_ip_i}{\alpha Q_i}}) \leq \beta_i < p_i$.

Figures (6)

  • Figure 1: The schematic diagram for system model.
  • Figure 2: The schematic diagram for the revenue function based on the coalition revenue threshold.
  • Figure 3: The variation of coalition overall utility with parameter $r$ under three scenarios.
  • Figure 4: The variation of coalition overall utility with task and UAV numbers for $N=20$ and $M=5$.
  • Figure 5: The variation of average utility of each task and UAV with the task and UAV numbers for $N=20$ and $M=5$.
  • ...and 1 more figures

Theorems & Definitions (8)

  • theorem 1
  • theorem 2
  • Definition 1
  • Definition 2
  • Definition 3
  • Definition 4
  • theorem 3
  • theorem 4