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Multi-AUV Underwater Cooperative Search Algorithm based on Biological Inspired Neurodynamics Model and Velocity Synthesis

Published online by Cambridge University Press:  20 May 2015

Xiang Cao
Affiliation:
(Laboratory of Underwater Vehicles and Intelligent Systems, Shanghai Maritime University, Shanghai, 200135, China)
Daqi Zhu*
Affiliation:
(Laboratory of Underwater Vehicles and Intelligent Systems, Shanghai Maritime University, Shanghai, 200135, China)
*

Abstract

Ocean currents impose a negative effect on Autonomous Underwater Vehicle (AUV) underwater target searches, which lengthens the search paths and consumes more energy and team effort. To solve this problem, an integrated algorithm is proposed to realise multi-AUV cooperative search in dynamic underwater environments with ocean currents. The proposed integrated algorithm combines the Biological Inspired Neurodynamics Model (BINM) and Velocity Synthesis (VS) method. Firstly, the BINM guides a team of AUVs to achieve target search in underwater environments; BINM search requires no specimen learning information and is thus easier to apply to practice, but the search path is longer because of the influence of ocean current. Next the VS algorithm offsets the effect of ocean current, and it is applied to optimise the search path for each AUV. Lastly, to demonstrate the effectiveness of the proposed integrated approach, simulation results are given in this paper. It is proved that this integrated algorithm can plan shorter search paths and thus the energy consumption is lower compared with BINM.

Type
Research Article
Copyright
Copyright © The Royal Institute of Navigation 2015 

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References

REFERENCES

Alvarez, A., Caiti, A. and Onken, R. (2004). Evolutionary Path Planning for Autonomous Underwater Vehicles in a Variable Ocean. IEEE Journal of Oceanic Engineering, 29, 418429.CrossRefGoogle Scholar
Couillard, M., Fawcett, J. and Davison, M. (2012). Optimizing Constrained Search Patterns for Remote Mine-hunting Vehicles. IEEE Journal of Oceanic Engineering, 37, 7584.CrossRefGoogle 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, 935948.Google Scholar
Gabriely, Y. and Rimon, E. (2003). Competitive On-line Coverage of Grid Environments by a Mobile Robot. Computational Geometry, 24, 197224.CrossRefGoogle Scholar
Gonzalez, E., Alvarez, O. and Diaz, Y. (2005). A Complete Coverage Algorithm. IEEE International Conference on Robotics and Automation, Barcelona, Spain.Google Scholar
Huang, H., Zhu, D.Q. and Ding, F. (2014). Dynamic Task Assignment and Path Planning for Multi-AUV System in Variable Ocean Current Environment. Journal of Intelligent & Robotic Systems, 74, 9991012.CrossRefGoogle Scholar
Jan, G. E., Chang, K. Y. and Parberry, I. (2008). Optimal Path Planning for Mobile Robot Navigation. IEEE Transactions on Mechatronics, 13, 451460.CrossRefGoogle Scholar
Kulkarni, I. S. and Pompili, D. (2010). Task Allocation for Networked Autonomous Underwater Vehicles in Critical Missions. IEEE Journal on Selected Areas in Communications, 28, 716727.CrossRefGoogle Scholar
Li, H. and Landa-Silva, D. (2011). An Adaptive Evolutionary Multi-objective Approach Based on Simulated Annealing. Evolutionary Computation, 19, 561595.CrossRefGoogle ScholarPubMed
Li, H., Yang, S. X. and Seto, M. L. (2009). Neural-network-based Path Planning for a Multirobot System with Moving Obstacles. IEEE Transactions on Systems, Man and Cybernetics, Part C:Applications and Reviews, 39, 410419.CrossRefGoogle Scholar
Lorenzo, B. and Glisic, S. (2013). Optimal Routing and Traffic Scheduling for Multihop Cellular Networks Using Genetic Algorithm. IEEE Transactions on Mobile Computing, 12, 22742288.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, 12791298.CrossRefGoogle Scholar
Masehian, E. and Nejad, A. H. (2010). A Hierarchical Decoupled Approach for Multi Robot Motion Planning on Trees. IEEE International Conference on Robotics and Automation, Anchorage, Alaska, USA.CrossRefGoogle Scholar
Millan, P., Orihuela, L., Jurado, I. and Rodriguez, F. R. (2014). Formation Control Autonomous Underwater Vehicles Subject to Communication Delays. IEEE Transactions on Control Systems Technology, 22, 770777.CrossRefGoogle 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, 20622077.Google ScholarPubMed
Ögmen, H. and Gagné, S. (1990). Neural Network Architectures for Motion Perception and Elementary Motion Detection in the Fly Visual System. Neural Networks, 3, 487505.CrossRefGoogle Scholar
Paley, D. A., Zhang, F. and Leonard, N.E. (2008). Cooperative Control for Ocean Sampling: the Glider Coordinated Control System. IEEE Transactions on Control Systems Technology, 16, 735744.CrossRefGoogle Scholar
Paull, L., Saeedi, S., Seto, M. and Li, H. (2014). AUV Navigation and Localization: a Review. IEEE Journal of Oceanic Engineering, 39, 131149.CrossRefGoogle Scholar
Polycarpou, M. M., Yang, Y. and Passino, K. M. (2001). Cooperative Control of Distributed Multi-agent Systems. IEEE Control Systems Magazine, 21, 127.Google Scholar
Roberge, V., Tarbouchi, M. and Labonte, G. (2013). Comparison of Parallel Genetic Algorithm and Particle Swarm Optimization for Real-time UAV Path Planning. IEEE Transactions on Industrial Informatics, 9, 132141.CrossRefGoogle Scholar
Sahbani, A., Sahar, E. K. and Philippe, B. (2012). An Overview of 3D Object Grasp Synthesis Algorithms. Robotics and Autonomous Systems, 60, 326336.Google Scholar
Soulignac, M. (2011). Feasible and Optimal Path Planning in Strong Current Fields. IEEE Transactions on Robot, 27, 8998.CrossRefGoogle Scholar
Yang, E. C. Y., Chao, P. C. P. and Cheng-Kuo, S. (2011). Optimal Control of an Under-actuated System for Landing with Desired Postures. IEEE Transactions on Control Systems Technology, 19, 248255.CrossRefGoogle Scholar
Yang, S. X. and Luo, C. M. (2004). A Neural Network Approach to Complete Coverage Path Planning. IEEE Transactions on Systems, Man and Cybernetics, Part B:Cybernetics, 34, 718725.CrossRefGoogle ScholarPubMed
Yang, Z. L., Zhu, Z. S. and Zhao, W. G. (2014). A Triangle Matching Algorithm for Gravity-aided Navigation for Underwater Vehicles. Journal of Navigation, 67, 227247.CrossRefGoogle Scholar
Yoerger, D. R., Jakuba, M., Bradley, A.M. and Bingham, B. (2007). Techniques for Deep Sea Near Bottom Survey Using an Autonomous Underwater Vehicle. International Journal of Robotics Research, 26, 4154.CrossRefGoogle Scholar
Yoon, S. and Qiao, C. (2011). Cooperative Search and Survey Using Autonomous Underwater Vehicles (AUVs). IEEE Transactions on Parallel and Distributed Systems, 22, 364379.CrossRefGoogle Scholar
Zhu, D. Q., Hua, X. and Sun, B. (2014). A Neurodynamics Control Strategy for Real-time Tracking Control of Autonomous Underwater Vehicles. Journal of Navigation, 67, 113127.CrossRefGoogle 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, 504514.Google Scholar
Zhu, Q., Liang, A. and Guan, H. (2011) A PSO-inspired Multi-robot Search Algorithm Independent of Global Information. Proceedings of the 2011 IEEE Symposium on Swarm Intelligence, Paris, France.CrossRefGoogle Scholar