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Task Assignment and Path Planning of a Multi-AUV System Based on a Glasius Bio-Inspired Self-Organising Map Algorithm

Published online by Cambridge University Press:  13 October 2017

Daqi Zhu ,
Yu Liu  and
Bing Sun
Daqi Zhu*
Affiliation:
(Laboratory of Underwater Vehicles and Intelligent Systems, Shanghai Maritime University, Haigang Avenue 1550, Shanghai, 201306, China)
Yu Liu
Affiliation:
(Laboratory of Underwater Vehicles and Intelligent Systems, Shanghai Maritime University, Haigang Avenue 1550, Shanghai, 201306, China)
Bing Sun
Affiliation:
(Laboratory of Underwater Vehicles and Intelligent Systems, Shanghai Maritime University, Haigang Avenue 1550, Shanghai, 201306, China)
*
(E-mail: [email protected])
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Abstract

For multi-Autonomous Underwater Vehicle (multi-AUV) system task assignment and path planning, a novel Glasius Bio-inspired Self-Organising Map (GBSOM) neural networks algorithm is proposed to solve relevant problems in a Three-Dimensional (3D) grid map. Firstly, a 3D Glasius Bio-inspired Neural Network (GBNN) model is established to represent the 3D underwater working environment. Using this model, the strength of neural activity is calculated at each node within the GBNN. Secondly, a Self-Organising Map (SOM) neural network is used to assign the targets to a set of AUVs and determine the order of the AUVs to access the target point. Finally, according to the magnitude of the neuron activity in the GBNN, the next AUV target point can be autonomously planned when the task assignment is completed. By repeating the above three steps, access to all target points is completed. Simulation and comparison studies are presented to demonstrate that the proposed algorithm can overcome the speed jump problem of SOM algorithms and path planning in the 3D underwater environments with static or dynamic obstacles.

Keywords

Multi-AUV SystemGBNNSOMTask assignmentPath planning
Type
Research Article
Information
The Journal of Navigation , Volume 71 , Issue 2 , March 2018 , pp. 482 - 496
Copyright
Copyright © The Royal Institute of Navigation 2017 

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References

REFERENCES

Aluizio, A.F.R. and Santana, O.V. (2015). Self-Organizing Map with Time-Varying Structure to Plan and Control Artificial Locomotion. IEEE Transactions on Neural Networks and Learning Systems, 26, 1594–1607.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.CrossRefGoogle Scholar
Glasius, R., Komoda, A. and Gielen, S. (1995). Neural network dynamics for path planning and obstacle avoidance. Neural Networks, 8, 125–133.CrossRefGoogle Scholar
Hopfield, J.J. and Tank, D.W. (1985). “Neural” computation of decisions in optimization problems. Springer-Verlag New York, Inc.CrossRefGoogle ScholarPubMed
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 Robot Systems, 74, 999–1012.CrossRefGoogle Scholar
Huang, H., Zhu, D.Q. and Yuan, F. (2012). Dynamic Task Assignment and Path Planning for Multi-AUV System in 2D Variable Ocean Current Environment. The 24th Chinese Control and Decision Conference, 23–25.CrossRefGoogle Scholar
Huang, Z., Zhu, D.Q. and Sun, B. (2016). A multi-AUV cooperative hunting method in 3-D underwater environment with obstacle. Engineering Applications of Artificial Intelligence, 50, 192–200.CrossRefGoogle Scholar
Jaillet, L. and Porta, J.M. (2013). Path planning under kinematic constraints by rapidly exploring manifolds. IEEE Transactions on Robotics, 29, 105–117.CrossRefGoogle Scholar
Javier, D.L., Dario, M. and Yadira, Q. (2015). Self-Organizing Techniques to Improve the Decentralized Multi-task Distribution in Multi-robot Systems. Neurocomputing, 163, 47–55.Google Scholar
Kohonen, T. (1998). The self-organizing map. Neurocomputing, 21, 1–6.CrossRefGoogle Scholar
Kulkarni, I.S. and Pompili, D. (2013). Task allocation networked autonomous underwater vehicles in critical missions. IEEE Journal on Selected Areas in Communications, 28, 716–727.CrossRefGoogle Scholar
Luo, C., Gao, J., Li, X., Mo, H. and Jiang, Q. (2014a). Sensor-based Autonomous Robot Navigation under Unknown Environments with Grid Map Representation. IEEE Symposium Series on Computational Intelligence (SSCI), IEEE, 98–104.CrossRefGoogle Scholar
Luo, C., Gao, J., Murphey, Y.L. and Jan, G.E. (2014b). A Computationally Efficient Neural Dynamics Approach to Trajectory Planning of An Intelligent Vehicle. IEEE International Joint Conference on Neural Networks (IJCNN), IEEE, 934–939.CrossRefGoogle Scholar
Luo, L.Z., Chakraborty, N. and Sycara, K. (2014). Provably-Good Distributed Algorithm for Constrained Multi-Robot Task Assignment for Grouped Tasks. IEEE Transactions on Robotics, 31, 19–30.CrossRefGoogle Scholar
Ren, S.X. and Mei, Y. (2013). Underwater glider task allocation based on the ant colony algorithm. Ninth International Conference on Natural Computation, 585–589.CrossRefGoogle Scholar
Sahar, T., Anis, K., Omar, C., Habib, Y., Hachemi, B., Mohamed-Foued, S. and Yasir, J. (2014). A Distributed Market-based Algorithm for the Multi-robot Assignment Problem. Procedia Computer Science, 32, 1108–1114.Google Scholar
Zhu, A. and Yang, S.X. (2006). A neural network approach to dynamic task assignment of multi-robots . IEEE Transactions on Neural Networks, 17, 1278–1287.Google Scholar
Zhu, A. and Yang, S.X. (2012). An improved SOM-based approach to dynamic task assignment of multi-robots. World Congress on Intelligent Control and Automation, Jinan, China, July, 2168–2173.Google Scholar
Zhu, D.Q., Huang, 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