Titre:	Mission specification and decomposition for multi-robot systems
Auteur(s):	Gil, Eric Bernd Rodrigues, Genaína Nunes Pelliccione, Patrizio Calinescu, Radu
metadata.dc.contributor.affiliation:	Universidade de Brasilia, Instituto de Ciências Exatas, Departamento de Ciência da Computação Universidade de Brasilia, Instituto de Ciências Exatas, Departamento de Ciência da Computação Gran Sasso Science Institute University of York, Department of Computer Science
Assunto::	Sistemas Multi-Robô Automação de processo Alocação de tarefas (Computação) Decomposição de missão (Computação) Robôs - programação
Date de publication:	28-fév-2023
Editeur:	Elsevier B.V.
Référence bibliographique:	GIL, Eric Bernd et al. Mission specification and decomposition for multi-robot systems. Robotics and Autonomous Systems, [S.l.], v. 163, 2023. DOI: https://doi.org/10.1016/j.robot.2023.104386. Disponível em: https://www.sciencedirect.com/science/article/pii/S0921889023000258?via%3Dihub. Acesso em: 13 nov. 2024.
Abstract:	Service robots are increasingly being used to perform missions comprising dangerous or tedious tasks previously executed by humans. However, their users—who know the environment and requirements for these missions—have limited or no robotics experience. As such, they often find the process of allocating concrete tasks to each robot within a multi-robot system (MRS) very challenging. Our paper introduces a framework for Multi-Robot mission Specification and decomposition (MutRoSe) that simplifies and automates key activities of this process. To that end, MutRoSe allows an MRS mission designer to define all relevant aspects of a mission and its environment in a high-level specification language that accounts for the variability of real-world scenarios, the dependencies between task instances, and the reusability of task libraries. Additionally, MutRoSe automates the decomposition of MRS missions defined in this language into task instances, which can then be allocated to specific robots for execution—with all task dependencies appropriately taken into account. We illustrate the application of MutRoSe and show its effectiveness for four missions taken from a recently published repository of MRS applications.
metadata.dc.description.unidade:	Instituto de Ciências Exatas (IE) Departamento de Ciência da Computação (IE CIC)
metadata.dc.description.ppg:	Programa de Pós-Graduação em Informática
Licença::	This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
DOI:	https://doi.org/10.1016/j.robot.2023.104386
metadata.dc.relation.publisherversion:	https://www.sciencedirect.com/science/article/pii/S0921889023000258?via%3Dihub
Collection(s) :	Artigos publicados em periódicos e afins

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