Flexible and Efficient Topological Approaches for a Reliable Robots Swarm Aggregation
Aggregation is a vital behavior when performing complex tasks in most of the swarm systems, such as swarm robotics systems. In this paper, three new aggregation methods, namely the distance-angular, the distance-cosine, and the distance-Minkowski k-nearest neighbor (k-NN) have been introduced. These...
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8769830/ |
| _version_ | 1818779393440350208 |
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| author | Belkacem Khaldi Fouzi Harrou Foudil Cherif Ying Sun |
| author_facet | Belkacem Khaldi Fouzi Harrou Foudil Cherif Ying Sun |
| author_sort | Belkacem Khaldi |
| collection | DOAJ |
| description | Aggregation is a vital behavior when performing complex tasks in most of the swarm systems, such as swarm robotics systems. In this paper, three new aggregation methods, namely the distance-angular, the distance-cosine, and the distance-Minkowski k-nearest neighbor (k-NN) have been introduced. These aggregation methods are mainly built on well-known metrics: the cosine, angular, and Minkowski distance functions, which are used here to compute distances among robots' neighbors. Relying on these methods, each robot identifies its k-nearest neighborhood set that will interact with. Then, in order to achieve the aggregation, the interactions sensing capabilities among the set members are modeled using a virtual viscoelastic mesh. Analysis of the results obtained from the ARGoS simulator shows a significant improvement in the swarm aggregation performance compared to the conventional distance-weighted k-NN aggregation method. Also, the aggregation performance of the methods is reported to be robust to partially faulty robots and accurate under noisy sensors. |
| first_indexed | 2024-12-18T11:59:54Z |
| format | Article |
| id | doaj.art-52a612ea23c74d1ab6cbd01ba1c93099 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-18T11:59:54Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-52a612ea23c74d1ab6cbd01ba1c930992022-12-21T21:08:58ZengIEEEIEEE Access2169-35362019-01-017963729638310.1109/ACCESS.2019.29306778769830Flexible and Efficient Topological Approaches for a Reliable Robots Swarm AggregationBelkacem Khaldi0Fouzi Harrou1https://orcid.org/0000-0002-2138-319XFoudil Cherif2Ying Sun3Department of Computer Science, LESIA Laboratory, University of Mohamed Khider, Biskra, AlgeriaComputer, Electrical, and Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi ArabiaDepartment of Computer Science, LESIA Laboratory, University of Mohamed Khider, Biskra, AlgeriaComputer, Electrical, and Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi ArabiaAggregation is a vital behavior when performing complex tasks in most of the swarm systems, such as swarm robotics systems. In this paper, three new aggregation methods, namely the distance-angular, the distance-cosine, and the distance-Minkowski k-nearest neighbor (k-NN) have been introduced. These aggregation methods are mainly built on well-known metrics: the cosine, angular, and Minkowski distance functions, which are used here to compute distances among robots' neighbors. Relying on these methods, each robot identifies its k-nearest neighborhood set that will interact with. Then, in order to achieve the aggregation, the interactions sensing capabilities among the set members are modeled using a virtual viscoelastic mesh. Analysis of the results obtained from the ARGoS simulator shows a significant improvement in the swarm aggregation performance compared to the conventional distance-weighted k-NN aggregation method. Also, the aggregation performance of the methods is reported to be robust to partially faulty robots and accurate under noisy sensors.https://ieeexplore.ieee.org/document/8769830/Swarm roboticsself-organized aggregation<italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">k</italic>-NNvirtual viscoelastic meshdistance metricspartially faulty robots |
| spellingShingle | Belkacem Khaldi Fouzi Harrou Foudil Cherif Ying Sun Flexible and Efficient Topological Approaches for a Reliable Robots Swarm Aggregation IEEE Access Swarm robotics self-organized aggregation <italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">k</italic>-NN virtual viscoelastic mesh distance metrics partially faulty robots |
| title | Flexible and Efficient Topological Approaches for a Reliable Robots Swarm Aggregation |
| title_full | Flexible and Efficient Topological Approaches for a Reliable Robots Swarm Aggregation |
| title_fullStr | Flexible and Efficient Topological Approaches for a Reliable Robots Swarm Aggregation |
| title_full_unstemmed | Flexible and Efficient Topological Approaches for a Reliable Robots Swarm Aggregation |
| title_short | Flexible and Efficient Topological Approaches for a Reliable Robots Swarm Aggregation |
| title_sort | flexible and efficient topological approaches for a reliable robots swarm aggregation |
| topic | Swarm robotics self-organized aggregation <italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">k</italic>-NN virtual viscoelastic mesh distance metrics partially faulty robots |
| url | https://ieeexplore.ieee.org/document/8769830/ |
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