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A Novel Classified Self-Organising Map Applied to Task Assignment.
- Source :
-
Journal of Navigation . Sep2020, Vol. 73 Issue 5, p1129-1145. 17p. - Publication Year :
- 2020
-
Abstract
- With the development of sensor technology, sensor nodes are increasingly being used in underwater environments. The strategy presented in this paper is designed to solve the problem of using a limited number of autonomous underwater vehicles (AUVs) to complete tasks such as data collection from sensor nodes when the number of AUVs is less than the number of target sensors. A novel classified self-organising map algorithm is proposed to solve the problem. First, according to the K-means algorithm, targets are classified into groups that are determined by the number of AUVs. Second, according to the self-organising map algorithm, AUVs are matched with groups. Third, each AUV is provided with the accessible order of the targets in the group. The novel classified self-organising map algorithm can be used not only to reduce the total energy consumption in a multi-AUV system, but also to give the most efficient accessible order of targets for AUVs. Results of simulations conducted to prove the applicability of the algorithm are given. [ABSTRACT FROM AUTHOR]
- Subjects :
- *K-means clustering
*AUTONOMOUS underwater vehicles
*ALGORITHMS
*ENERGY consumption
Subjects
Details
- Language :
- English
- ISSN :
- 03734633
- Volume :
- 73
- Issue :
- 5
- Database :
- Academic Search Index
- Journal :
- Journal of Navigation
- Publication Type :
- Academic Journal
- Accession number :
- 145030644
- Full Text :
- https://doi.org/10.1017/S037346332000020X