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Game theoretic self-organization in multi-satellite distributed task allocation

delete2021-05-01
delete19
PRE
AI
S
Sun, Changhao *
X
Xiaochu Wang
H
Huaxin Qiu
Q
Qingrui Zhou
DOI：10.1016/j.ast.2021.106650delete
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Abstract

Abstract

En 中文
This paper addresses self-organized task allocation in multi-satellite systems by formulating it as a potential game and proposing a distributed task allocation algorithm (DT2A) via learning in games. We show that each Nash equilibrium of the game constitutes a task cover that maximizes the number of executed tasks. Moreover, we also prove that the proposed DT2A converges with probability 1to near optimal assignments in finite time. Numerical experiments demonstrate the algorithm robustness against incidental disturbances and reveal the positive effect of the memory length on solution efficiency refinement. Comparison experiments in both small-scale and large-scale scenarios highlight the superiority of the presented methodology over existing typical methods that are distributed or centralized. (c) 2021 Elsevier Masson SAS. All rights reserved.
Keywords:
Multi-satellite system
Distributed task allocation
Learning in games
Self organization
Nash equilibrium
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Journal

Aerospace Science and Technology cover
Aerospace Science and Technology
IF:
5.8
Papers:
1.0W
Citations:
3.0W

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