| Title |
Multi-robot task allocation clustering based on game theory |
| Authors |
Martin, Javier G. , MUROS, FRANCISCO JAVIER, Maestre, Jose Maria , Camacho, Eduardo F. |
| External publication |
Si |
| Means |
ROBOTICS AND AUTONOMOUS SYSTEMS |
| Scope |
Article |
| Nature |
Científica |
| JCR Quartile |
2 |
| SJR Quartile |
1 |
| SJR Impact |
1.303 |
| Web |
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85144537183&doi=10.1016%2fj.robot.2022.104314&partnerID=40&md5=182593b321ea70411f3f06ed0130dbe2 |
| Publication date |
01/03/2023 |
| ISI |
000911191200001 |
| Scopus Id |
2-s2.0-85144537183 |
| DOI |
10.1016/j.robot.2022.104314 |
| Abstract |
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/). |
| Keywords |
Multi-robot systems (MRS); Multi-robot task allocation (MRTA); Clustering; Task planning; Cooperative game theory; Shapley value |
| Universidad Loyola members |
|
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