Hostname: page-component-76fb5796d-dfsvx Total loading time: 0 Render date: 2024-04-27T00:49:44.842Z Has data issue: false hasContentIssue false
  • English
  • Français

A Novel Classified Self-Organising Map Applied to Task Assignment

Published online by Cambridge University Press:  27 May 2020

Yun Qu  and
Daqi Zhu Open the ORCID record for Daqi Zhu [Opens in a new window]
Yun Qu
Affiliation:
(Shanghai Engineering Research Center of Intelligent Maritime Search & Rescue and Underwater Vehicles, Shanghai)
Daqi Zhu*
Affiliation:
(Shanghai Engineering Research Center of Intelligent Maritime Search & Rescue and Underwater Vehicles, Shanghai)
*
(E-mail: zdq367@aliyun.com)
Get access Rights & Permissions [Opens in a new window]

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.

Keywords

Autonomous Underwater Vehicles (AUVs)Classified Self-Organising MapMatchedOrder
Type
Research Article
Information
The Journal of Navigation , Volume 73 , Issue 5 , September 2020 , pp. 1129 - 1145
Check for updates
Copyright
Copyright © The Royal Institute of Navigation 2020

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Akkiraju, R., Keskinocak, P., Murthy, S. and Wu, F. (2001). An agent-based approach for scheduling multiple machines. Applied Intelligence, 14(2), 135–144.CrossRefGoogle Scholar
Allotta, B., Caiti, A., Costanzi, R., Fanelli, F., Fenucci, D., Meli, E. and Ridolfi, A. (2016). A new AUV navigation system exploiting unscented Kalman filter. Ocean Engineering, 113, 121–132.CrossRefGoogle Scholar
Burlutskiy, N., Touahmi, Y. and Lee, B. L. (2012). Power efficient formation configuration for centralized leader-follower AUVs control. Journal of Marine Science and Technology, 17(3), 315–329.CrossRefGoogle Scholar
Cao, X., Zhu, D. and Yang, S. X. (2016). Multi-AUV target search based on bioinspired neurodynamics model in 3-D underwater environments. IEEE Transactions on Neural Networks and Learning Systems, 27(11), 2364–2374.CrossRefGoogle ScholarPubMed
Chang, Z., Bian, X., Yan, Z. and Wang, Y. (2004). Study of AUV's real-time task coordination method. Journal of Harbin Engineering University, 25(4), 472–476.Google Scholar
Gan, W., Zhu, D., Hu, Z., Yang, L., Shi, X. and Chen, Y. (2019). Model predictive adaptive constraint tracking control for underwater vehicles. IEEE Transactions on Industrial Electronics, doi:10.1109/TIE.2019.2941132Google Scholar
Huang, H., Zhu, D. and Yuan, F. (2012). Dynamic Task Assignment and Path Planning for Multi-AUV System in 2D Variable Ocean Current Environment. Chinese Control and Decision Conference(CCDC), Taiyuan, China.CrossRefGoogle Scholar
Kohonen, T., Kaski, S., Lagus, K., Salojarvi, J., Honkela, J., Paatero, V. and Saarela, A. (2000). Self organization of a massive document collection. IEEE Transactions on Neural Networks, 11(3), 574–585.CrossRefGoogle ScholarPubMed
Li, J. and Zhang, K. (2017). Heterogeneous multi-AUV cooperative task allocation based on improved contract net algorithm. Journal of Unmanned Undersea Systems, 25(5), 418–423.Google Scholar
Li, J., Zhang, R. and Yang, Y. (2014) Research on Route Obstacle Avoidance Task Planning Based on Differential Evolution Algorithm for AUV. International Conference in Swarm Intelligence(ICSI), Hefei, China.CrossRefGoogle Scholar
Liu, Y. and Zhu, D. (2017). Task assignment and path planning of AUV system based on Glasius bio-inspired self-organizing map neural network algorithm. System Simulation Technology, 13(3), 231–234.Google Scholar
Loebis, D., Sutton, R., Chudley, J. and Naeem, W. (2004). Adaptive tuning of a Kalman filter via fuzzy logic for an intelligent AUV navigation system. Control Engineering Practice, 12(12), 1531–1539.CrossRefGoogle Scholar
Muslim, M. A., Ishikawa, M. and Furukawa, T. (2007). Task segmentation in a mobile robot by MnSOM: a new approach to training expert modules. Neural Computing and Applications, 16(6), 571–580.CrossRefGoogle Scholar
Sariel, S., Balch, T. and Stack, J. (2006). Distributed multi-AUV coordination in naval mine countermeasure missions. IEEE Robotics and Automation Magazine, 15(1), 45–52.CrossRefGoogle Scholar
Sotzing, C. C. and Lane, D. M. (2010). Improving the coordination efficiency of limited communication multi-AUV operations using a multi-agent architecture. Journal of Field Robotics, 27(4), 412–429.CrossRefGoogle Scholar
Sujit, B. and Beard, R. (2007). Distributed Sequential Auctions for Multiple UAV Task Allocation. American Control Conference (ACC), New York, USA.CrossRefGoogle Scholar
Tambouratzis, G. (2006). Assessing the effectiveness of feature groups in author recognition tasks with the SOM model. IEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews, 36(2), 249–259.CrossRefGoogle Scholar
Tsiogkas, N., Papadimitriou, G., Saigol, Z. and Lane, D. (2014). Efficient Multi-AUV Cooperation Using Semantic Knowledge Representation for Underwater Archaeology Missions. Oceans - St. John's, St. John's, NL, Canada.Google Scholar
Wang, Z. and Feng, X. (2011) A Cooperative Simulation System for AUV Based on Multi-Agent. IEEE International Conference on Virtual Reality and Visualization(ICVRV), Beijing, China.CrossRefGoogle Scholar
Wang, C., Chen, C. and Hung, K. N. (2015). Toward a new task assignment and path evolution (TAPE) for missile defense system (MDS) using intelligent adaptive SOM with recurrent neural networks (RNNs). IEEE Transactions on Cybernetics, 45(6), 1134–1145.CrossRefGoogle Scholar
Wu, Y., Ta, X., Xiao, R., Wei, Y., An, D. and Li, D. (2019). Survey of underwater robot positioning navigation. Applied Ocean Research, doi:10.1016/j.apor.2019.06.002CrossRefGoogle Scholar
Xiang, X., Liu, C., Su, H. and Zhang, Q. (2017). On decentralized adaptive full-order sliding mode control of multiple UAVs. ISA Transactions, 71(2), 196–205.CrossRefGoogle ScholarPubMed
Yu, C., Xiang, X., Lapierre, L. and Zhang, Q. (2018). Robust magnetic tracking of subsea cable by AUV in the presence of sensor noise and ocean currents. IEEE Journal of Oceanic Engineering, 43(2), 311–322.CrossRefGoogle Scholar
Zhang, Q., Zhang, J., Chemori, A. and Xiang, X. (2018). Virtual submerged floating operational system for robotic manipulation. Complexity, doi:10.1155/2018/9528313CrossRefGoogle Scholar
Zhu, D., Huang, H. and Yang, S. X. (2012). Dynamic task assignment and path planning of multi-AUV system based on an improved self-organizing map and velocity synthesis method in three-dimensional underwater workspace. IEEE Transactions on Cybernetics, 43(2), 504–514.Google Scholar
Zhu, D., Liu, Y. and Sun, B. (2017). Task assignment and path planning of a multi-AUV system based on a Glasius bio-inspired self-organising map algorithm. Journal of Navigation, 71(2), 1–15.Google Scholar
Zhu, D., Qu, Y. and Yang, S. X. (2019). Multi-AUV SOM task allocation algorithm considering initial orientation and ocean current environment. Frontiers of Information Technology and Electronic Engineering, 20(3), 330–341.CrossRefGoogle Scholar