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Multi-AUV Cooperative Hunting Control with Improved Glasius Bio-inspired Neural Network

Published online by Cambridge University Press:  05 November 2018

Mingzhi Chen  and
Daqi Zhu Open the ORCID record for Daqi Zhu [Opens in a new window]
Mingzhi Chen
Affiliation:
(Laboratory of Underwater Vehicles and Intelligent Systems, Shanghai Maritime University, Haigang Avenue 1550, Shanghai, 201306, China)
Daqi Zhu*
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

Cooperative hunting with multiple Autonomous Underwater Vehicles (AUVs) not only needs the AUVs to cooperate, but also demands real-time path planning to catch up with evading targets. In this paper a time-based alliance mechanism to form efficient dynamic hunting alliances is proposed. After that, during the active hunting stage, an improved neural network model based on a Glasius Bio-inspired Neural Network (GBNN) is presented for path planning to immediately achieve tracking of an intelligent target. This study shows that the improved GBNN model has good performance in real-time hunting path planning. From the simulation studies as described in this paper, both the hunting alliance formation mechanism and the proposed real-time hunting path planning strategy show their advantages. The results show that the improved GBNN model proposed in this paper can work well in the control of multiple AUVs to hunt for intelligent evading targets in environments containing obstacles.

Keywords

Multi-AUV SystemHunting ControlImproved Glasius Bio-inspired Neural Network (GBNN)Time-based AllianceReal-time Path planning
Type
Research Article
Information
The Journal of Navigation , Volume 72 , Issue 3 , May 2019 , pp. 759 - 776
Copyright
Copyright © The Royal Institute of Navigation 2018 

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References

REFERENCES

Agreev, D.A. (1998). Neural network implementation for the optimal path problem. Journal of Computer and System Sciences International, 37, 118–125.Google Scholar
Blidberg, R.D. (2001). The Development of Autonomous Underwater Vehicles (AUV); A Brief Summary. IEEE International Conference on Robotics and Automation, Seoul, Korea, May, 209–212.Google Scholar
Cao, X. and Yu, A.L. (2017). Multi-AUV Cooperative Target Search Algorithm in 3-D Underwater Workspace. Journal of Navigation, 70, 1293–1311.Google Scholar
Cao, X., Huang, Z., and Zhu, D. (2015). AUV cooperative hunting algorithm based on bio-inspired neural network for path conflict state. 2015 IEEE International Conference on Information and Automation, Lijiang, China, August, 1821–1826.Google Scholar
Chen, M., and Zhu, D. (2018). A Novel Cooperative Hunting Algorithm for Inhomogeneous Multiple Autonomous Underwater Vehicles. IEEE Access, 6, 7818–7828.Google Scholar
Glasius, R., Komoda, A. and Gielen, S.C.A.M. (1994). Population coding in a neural net for trajectory formation. Network: Computation in Neural Systems, 5, 549–563.Google Scholar
Glasius, R., Komoda, A. and Gielen, S.C.A.M. (1995). Neural network dynamics for path planning and obstacle avoidance. Neural Network, 8, 125–133.Google Scholar
Glasius, R., Komoda, A. and Gielen, S.C.A.M. (1996). A biologically inspired neural net for trajectory formation and obstacle avoidance. Biological Cybernetics, 74, 511–520.Google Scholar
Grossberg, S. (1988). Nonlinear neural networks: Principles, mechanisms, and architectures. Neural networks, 1, 17–61.Google Scholar
Ishiwaka, Y., Sato, T. and Kakazu, Y. (2003). An approach to the pursuit problem on a heterogeneous multiagent system using reinforcement learning. Robotics & Autonomous Systems, 43, 245–256.Google Scholar
Joung, T.H., Lee, J.H., Nho, I. and Kim, B.J. (2009). A study on the design and manufacturing of a deep-sea unmanned underwater vehicle based on structural reliability analysis. Ships & Offshore Structures, 4, 19–29.Google Scholar
Krieg, M. and Mohseni, K. (2010). Dynamic Modeling and Control of Biologically Inspired Vortex Ring Thrusters for Underwater Robot Locomotion. IEEE Transactions on Robotics, 26, 542–554.Google Scholar
Lebedev, D.V., Steil, J.J. and Ritter, H.J. (2005). The dynamic wave expansion neural network model for robot path planning in time-varying environments. Neural Networks, 18, 267–285.Google Scholar
Li, Jun, Pan, Q.S. and Hong, B.R. (2010). A New Approach of Multi-robot Cooperative Pursuit Based on Association Rule Data Mining. International Journal of Advanced Robotic Systems, 6, 1169–1174.Google Scholar
Li, Jun, Pan, Q.S., Hong, B.R. and Li, M.H. (2009). Multi-robot Cooperative Pursuit Based on Association Rule Data Mining. International Conference on Fuzzy Systems and Knowledge Discovery, Tianjin, China, August, 303–308.Google Scholar
Monroy, J. A., Campos, E. and Torres, J.A. (2017). Attitude Control of a Micro AUV through an Embedded System. IEEE Latin America Transactions, 15, 603–612.Google Scholar
Nguyen, B. and Hopkin, D. (2005). Modeling Autonomous Underwater Vehicle (AUV) operations in mine hunting. Oceans, 1, 533–538.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
Ni, J.J., Yang, L., Wu, L., and Fan, X. (2018). An improved spinal neural system-based approach for heterogeneous AUVs cooperative hunting. International Journal of Fuzzy Systems, 20, 672–686.Google Scholar
Ni, J.J., Wu, L., Shi, P., and Yang, S.X. (2017). A dynamic bioinspired neural network based real-time path planning method for autonomous underwater vehicles. Computational Intelligence & Neuroscience, 2017, 1–16.Google Scholar
Song, Y., Li, Y.B., Li, C.H., and Ma, X. (2015). Mathematical modeling and analysis of multirobot cooperative hunting behaviors. Journal of Robotics, 2015, 1–8.Google Scholar
Williams, D.P. (2010). On optimal AUV track-spacing for underwater mine detection. IEEE International Conference on Robotics and Automation, Anchorage, Alaska, USA, May, 4755–4762.Google Scholar
Xia, Y. and Wang, J. (2000). A discrete-time recurrent neural network for shortest-path routing. IEEE Transactions on Automatic Control, 45, 2129–2134.Google Scholar
Yamaguchi, H. (1998). A cooperative hunting behavior by multiple nonholonomic mobile robots. IEEE International Conference on Systems, Man, and Cybernetics, San Diego, CA, USA, October, 3347–3352.Google Scholar
Yamaguchi, H. (1999). A cooperative hunting behavior by mobile-robot troops. The International Journal of Robotics Research, 18, 931–940.Google Scholar
Yang, S.X. and Meng, M. (2000). An efficient neural network approach to dynamic robot path planning. Neural Networks, 13, 143–148.Google Scholar
Yang, S.X. and Meng, M. (2001). Neural network approaches to dynamic collision-free trajectory generation. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 31, 302–318.Google Scholar
Yang, S.X. and Meng, M. (2003). Real-time collision-free path planning of a mobile robot using a neural dynamics-based approach. IEEE Transactions on Neural Networks, 14, 1541–1552.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, 482–496.Google Scholar
Zhu, D., Lv, R., Cao, X., and Yang, S.X. (2015). Multi-AUV Hunting Algorithm Based on Bio-inspired Neural Network in Unknown Environments. International Journal of Advanced Robotic Systems, 12, 1–12.Google Scholar