Abstract
Multi-robot task allocation (MRTA) problems have been studied extensively in the past decades. As a result, several classifications have been proposed in the literature targeting different aspects of MRTA, with often a few commonalities between them. The goal of this paper is twofold. First, a comprehensive overview of early work on existing MRTA taxonomies is provided, focusing on their differences and similarities. Second, the MRTA problem is modelled using an Entity-Relationship (ER) conceptual formalism to provide a structured representation of the most relevant aspects, including the ones proposed within previous taxonomies. Such representation has the advantage of (i) representing MRTA problems in a systematic way, (ii) providing a formalism that can be easily transformed into a software infrastructure, and (iii) setting the baseline for the definition of knowledge bases, that can be used for automated reasoning in MRTA problems.
This work was supported by the Aggregate Farming in the Cloud (AFarCloud) European project, with project number 783221 (Call: H2020-ECSEL-2017-2), DPAC research profile funded by KKS (20150022), the FIESTA project funded by KKS, and the UNICORN project. Projects are supported by ECSEL JU and the VINNOVA.
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Notes
- 1.
All entities have an ID attribute, that uniquely distinguishes between instances of the same entity. The ID is not further discussed in this paper.
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Miloradović, B., Frasheri, M., Cürüklü, B., Ekström, M., Papadopoulos, A.V. (2019). TAMER: Task Allocation in Multi-robot Systems Through an Entity-Relationship Model. In: Baldoni, M., Dastani, M., Liao, B., Sakurai, Y., Zalila Wenkstern, R. (eds) PRIMA 2019: Principles and Practice of Multi-Agent Systems. PRIMA 2019. Lecture Notes in Computer Science(), vol 11873. Springer, Cham. https://doi.org/10.1007/978-3-030-33792-6_32
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