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Title: | Towards a Distributed Solution to the Multi-Robot Task Allocation Problem with Energetic and Spatiotemporal Constraints | Authors: | Zitouni, Farouq Harous, Saad Maamri, Ramdane |
Affiliations: | Faculty of Information and Communication Technology (ICT) Faculty of Information and Communication Technology (ICT) |
Keywords: | multi-robot systems;parallel distributed guided genetic algorithms;spatial constraints;temporal constraints;objective function;energetic constraints;multi-robot task allocation | Issue Date: | Dec-2019 | Publisher: | {AGHU} University of Science and Technology Press | Journal: | Computer Science | Volume: | 21 | Issue: | 1 | Abstract: | 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. |
URI: | https://doi.org/10.7494/csci.2020.21.1.3427 http://dspace.univ-constantine2.dz/handle/123456789/83 |
DOI: | 10.7494/csci.2020.21.1.3427 | Rights: | Attribution-NonCommercial-ShareAlike 3.0 United States |
Appears in Collections: | Journal Articles |
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Towards_a_Distributed_Solution.pdf | 577.83 kB | Adobe PDF | View/Open |
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