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Multi-AUV Cooperative Target Search Algorithm in 3-D Underwater Workspace

Published online by Cambridge University Press:  30 June 2017

Xiang Cao  and
A-long Yu
Xiang Cao*
Affiliation:
(School of Physics and Electronic Electrical Engineering, Huaiyin Normal University, Huaian 223300, China)
A-long Yu
Affiliation:
(School of Physics and Electronic Electrical Engineering, Huaiyin Normal University, Huaian 223300, China)
*
(E-mail: [email protected])
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Abstract

To improve the efficiency of multiple Autonomous Underwater Vehicles (multi-AUV) cooperative target search in a Three-Dimensional (3D) underwater workspace, an integrated algorithm is proposed by combining a Self-Organising Map (SOM), neural network and Glasius Bioinspired Neural Network (GBNN). With this integrated algorithm, the 3D underwater workspace is first divided into subspaces dependent on the abilities of the AUV team members. After that, tasks are allocated to each subspace for an AUV by SOM. Finally, AUVs move to the assigned subspace in the shortest way and start their search task by GBNN. This integrated algorithm, by avoiding overlapping search paths and raising the coverage rate, can reduce energy consumption of the whole multi-AUV system. The simulation results show that the proposed algorithm is capable of guiding multi-AUV to achieve a multiple target search task with higher efficiency and adaptability compared with a more traditional bioinspired neural network algorithm.

Keywords

Cooperative target searchMulti-AUV. Self-organising mapGlasius bioinspired neural network
Type
Research Article
Information
The Journal of Navigation , Volume 70 , Issue 6 , November 2017 , pp. 1293 - 1311
Copyright
Copyright © The Royal Institute of Navigation 2017 

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References

REFERENCES

Cai, Y.F. and Yang, S.X. (2013). An improved PSO-based approach with dynamic parameter tuning for cooperative multi-robot target searching in complex unknown environments. International Journal of Control, 86, 1720–1732.Google Scholar
Cai, Y.F., Yang, S.X. and Xu, X. (2013). A hierarchical reinforcement learning based approach to multi-robot cooperation for target searching in unknown environments. Control and Intelligent Systems, 41, 1–12.Google Scholar
Cao, X. and Zhu, D.Q. (2015). Multi-AUV underwater cooperative search algorithm based on biological inspired neurodynamics model and velocity synthesis. Journal of Navigation, 68, 1075–1087.Google Scholar
Cao, X., Zhu, D.Q. and Yang, S.X. (2016). Multi-AUV cooperative target search based on biological inspired neurodynamics model in 3-D underwater environments. IEEE Transactions on Neural Networks and Learning Systems, 27, 1–11.CrossRefGoogle Scholar
Cao, X. and Zhu, D.Q. (2017). Multi-AUV task assignment and path planning with ocean current based on biological inspired self-organizing map and velocity synthesis algorithm. Intelligent Automation & Soft Computing, 23, 31–39.Google Scholar
Couillard, M., Fawcett, J. and Davison, M. (2012). Optimizing constrained search patterns for remote mine-hunting vehicles. IEEE Journal of Oceanic Engineering, 37, 75–84.Google Scholar
Cui, R.X., Ge, S.S., How, B.V.E. and Choo, Y.S. (2010). Leader-follower formation control of underactuated autonomous underwater vehicles. Ocean Engineering, 37, 1491–1502.Google Scholar
Fiorelli, E., Leonard, N., Bhatta, P., Paley, D., Bachmayer, R. and Fratantoni, D. (2006). Multi-AUV control and adaptive sampling in Monterey Bay. IEEE Journal of Oceanic Engineering, 31, 935–948.Google Scholar
Gabriely, Y. and Rimon, E. (2003). Competitive on-line coverage of grid environments by a mobile robot. Computational Geometry, 24, 197–224.Google Scholar
Glasius, R., Komoda, A. and Gielen, S. (1995). Neural network dynamics for path planning and obstacle avoidance. Neural Networks, 8, 125–133.Google Scholar
Glasius, R., Komoda, A. and Gielen, S. (1996). A biologically inspired neural net for trajectory formation and obstacle avoidance. Biological Cybernetics, 74, 511–520.CrossRefGoogle ScholarPubMed
Gonzlez, E., Alvarez, O. and Diaz, Y. (2005). A complete coverage algorithm, IEEE International Conference on Robotics and Automation, Barcelona, Spain, 2040–2044.Google Scholar
Hendzel, Z. (2005). Collision free path planning and control of wheeled mobile robot using self-organizing map. Technical Sciences, 53, 39–47.Google Scholar
Kohonen, T. (1982). Analysis of a simple self-organizing process. Biological Cybernetics, 44, 135–140.Google Scholar
Lapierre, L. and Jouvencel, B. (2008). Robust nonlinear path-following control of an AUV. IEEE Journal of Oceanic Engineering, 33, 89–102.CrossRefGoogle Scholar
Luo, C.M. and Yang, S.X. (2008). A bioinspired neural network for real-time concurrent map building and complete coverage robot navigation in unknown environments. IEEE Transactions on Neural Networks, 19, 1279–1298.Google Scholar
Luo, C.M., Gao, J.Y., Li, X.D., Mo, H.W. and Jiang, Q.M. (2014b). Sensor-based autonomous robot navigation under unknown environments with grid map representation. IEEE Symposium on Swarm Intelligence, 1–7.Google Scholar
Luo, C.M., Gao, J.Y., Murphey, Y.L. and Jan, G.E. (2014a). A computationally efficient neural dynamics approach to trajectory planning of an intelligent vehicle. International Joint Conference on Neural Networks, Beijing, China, 934–939.Google Scholar
Lynch, B. and Ellery, A. (2014). Efficient control of an AUV-manipulator system: an application for the exploration of Europa. IEEE Journal of Oceanic Engineering, 39, 552–570.CrossRefGoogle Scholar
Matsuda, T., Maki, T. and Sakamaki, T. (2012). Performance analysis on a navigation method of multiple AUVs for wide area survey. Marine Technology Society Journal, 46, 45–55.CrossRefGoogle Scholar
Miyata, N., Ota, J., Arai, T. and Asama, H. (2002). Cooperative transport by multiple mobile robots in unknown static environments associated with real-time task assignment. IEEE Transactions on Robotics and Automation, 18, 769–780.Google Scholar
Ni, J.J. and Yang, S.X. (2011). Bioinspired neural network for real-time cooperative hunting by multirobots in unknown environments. IEEE Transactions on Neural Networks, 22, 2062–2077.Google Scholar
Paull, L., Saeedi, S., Seto, M. and Li, H. (2014). AUV navigation and localization: a review. IEEE Journal of Oceanic Engineering, 39, 131–149.Google Scholar
Polycarpou, M.M., Yang, Y. and Passino, K.M. (2001). Cooperative control of distributed multi-agent systems. IEEE Control Systems Magazine, 21, 1–27.Google Scholar
Reeve, R. and Hallam, J. (2005). An analysis of neural models for walking control. IEEE Transactions on Neural Networks, 16, 733–742.Google Scholar
Woolsey, C. and Techy, L. (2009). Cross-track control of a slender, underactuated AUV using potential shaping. Ocean Engineering, 36, 82–91.Google Scholar
Yang, S.X. and Meng, M. (2003). Real-time collision-free motion planning of a mobile robot using a neural dynamics-based approach. IEEE Transactions on Neural Networks, 14, 1541–1552.Google Scholar
Yoon, S. and Qiao, C. (2011). Cooperative search and survey using autonomous underwater vehicles (AUVs). IEEE Transactions on Parallel and Distributed Systems, 22, 364–379.Google Scholar
Zhu, A. and Yang, S.X. (2006). A neural network approach to dynamic task assignment of multi-robot. IEEE Transactions on Neural Networks, 17, 1278–1287.Google Scholar
Zhu, D.Q., Huan, H. and Yang, S.X. (2013). 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, 504–514.Google Scholar