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This paper tackles the Multi-Robot Task Allocation problem. It consists of two distinct sets: a set of tasks (requiring resources), and a set of robots (offering resources). Then, the tasks are allocated to robots while optimizing a certain objective function subject to some constraints; e.g., allocating the maximum number of tasks, minimizing the distances traveled by the robots, etc. Previous works mainly optimized the temporal and spatial constraints, but no work focused on energetic constraints. Our main contribution is the introduction of energetic constraints on multi-robot task allocation problems. In addition, we propose an allocation method based on parallel distributed guided genetic algorithms and compare it to two state-of-the-art algorithms. The performed simulations and obtained results show the effectiveness and scalability of our solution, even in the case of a large number of robots and tasks. We believe that our contribution is applicable in many contemporary areas of research such as smart cities and related topics.
Wydawca
Czasopismo
Rocznik
Tom
Strony
3--24
Opis fizyczny
Bibliogr. 61 poz., rys., tab.
Twórcy
autor
- Kasdi Merbah University, Department of Computer Science, Ouargla, Abdelhamid Mehri University, LIRE Laboratory, Constantine, Algeria
autor
- UAE University, Department of Computer Science and Software Engineering, Abu Dhabi, United Arab Emirates
autor
- Abelhamid Mehri University, Department of Computer Science, Constantine; Abdelhamid Mehri University, LIRE Laboratory, Constantine, Algeria
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Uwagi
PL
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).
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Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-1ef5d4c0-7363-48f6-8a10-aaa2fbbc1941