Flexible and Efficient Topological Approaches for a Reliable Robots Swarm Aggregation
| Autor: | Ying Sun, Fouzi Harrou, Foudil Cherif, Belkacem Khaldi |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
0301 basic medicine
Swarm robotics General Computer Science Computer science distance metrics Topology 01 natural sciences k-nearest neighbors algorithm Computer Science::Robotics Set (abstract data type) 03 medical and health sciences virtual viscoelastic mesh General Materials Science Robot kinematics partially faulty robots 010401 analytical chemistry General Engineering Minkowski distance Order (ring theory) Swarm behaviour self-organized aggregation 0104 chemical sciences 030104 developmental biology Robot lcsh:Electrical engineering. Electronics. Nuclear engineering lcsh:TK1-9971 |
| Zdroj: | IEEE Access, Vol 7, Pp 96372-96383 (2019) |
| ISSN: | 2169-3536 |
| DOI: | 10.1109/access.2019.2930677 |
| Popis: | 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 $\mathit {k}$ -nearest neighbor ( $\mathit {k}$ -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 $\mathit {k}$ -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 $\mathit {k}$ -NN aggregation method. Also, the aggregation performance of the methods is reported to be robust to partially faulty robots and accurate under noisy sensors. |
| Databáze: | OpenAIRE |
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