Multi-robot task allocation clustering based on game theory

被引:11
|
作者
Martin, Javier G. [1 ]
Muros, Francisco Javier [1 ]
Maestre, Jose Maria [1 ]
Camacho, Eduardo F. [1 ]
机构
[1] Univ Seville, Dept Syst & Automat Engn, Seville, Spain
基金
欧盟地平线“2020”;
关键词
Multi-robot systems (MRS); Multi-robot task allocation (MRTA); Clustering; Task planning; Cooperative game theory; Shapley value; SHAPLEY VALUE; MULTIOBJECTIVE OPTIMIZATION; EFFICIENT COMPUTATION; COALITION-FORMATION; COORDINATION; ASSIGNMENT; ALGORITHM; POWER; TAXONOMY;
D O I
10.1016/j.robot.2022.104314
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
A cooperative game theory framework is proposed to solve multi-robot task allocation (MRTA) problems. In particular, a cooperative game is built to assess the performance of sets of robots and tasks so that the Shapley value of the game can be used to compute their average marginal contribution. This fact allows us to partition the initial MRTA problem into a set of smaller and simpler MRTA subproblems, which are formed by ranking and clustering robots and tasks according to their Shapley value. A large-scale simulation case study illustrates the benefits of the proposed scheme, which is assessed using a genetic algorithm (GA) as a baseline method. The results show that the game theoretical approach outperforms GA both in performance and computation time for a range of problem instances.(c) 2022 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
引用
收藏
页数:11
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