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Multi-AUV task assignment and path planning with ocean current based on biological inspired self-organizing map and velocity synthesis algorithm


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Abstract

An integrated multiple autonomous underwater vehicles (multi-AUV) dynamic task assignment and path planning algorithm is proposed for three-dimensional underwater workspace with ocean current. The proposed algorithm in this paper combines biological inspired self-organizing map (BISOM) and a velocity synthesis algorithm (VS). The goal is to control a team of AUVs to visit all targets, while guaranteeing AUV2019s motion can offset the impact of ocean currents. First, the SOM neural network is developed to assign a team of AUVs to achieve multiple target locations in underwater environments. Then to avoid obstacle autonomously for each AUV to visit the corresponding target, the biological inspired neurodynamics model (BINM) is used to update weights of the winner of SOM, and realize AUVs path planning autonomously. Lastly, the velocity synthesis algorithm is applied to optimize a path for each AUV to visit the corresponding target in dynamic environment with the ocean current. To demonstrate the effectiveness of the proposed algorithm, simulation results are given in this paper. Undoubtedly, the proposed algorithm is capable of dealing with task assignment and path planning in different environment. The path of the AUV is not affected by the effects of ocean currents and there are no great changes.


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Pages

Total Pages: 9
Pages: 31-39

DOI
10.1080/10798587.2015.1118277


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Published

Volume: 23
Issue: 1
Year: 2015

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References

Akkiraju, Rama et al. Applied Intelligence 14.2 (2001): 135-144. Crossref. Web. https://doi.org/10.1023/A:1008363208898

Cao, Xiang, and Daqi Zhu. "Multi-AUV Underwater Cooperative Search Algorithm Based on Biological Inspired Neurodynamics Model and Velocity Synthesis." Journal of Navigation 68.06 (2015): 1075-1087. Crossref. Web. https://doi.org/10.1017/S0373463315000351

Chow B. Assigning closely spaced targets to multiple autonomous underwater vehicles

Chu, Zhen-zhong, and Ming-jun Zhang. "Fault Reconstruction of Thruster for Autonomous Underwater Vehicle Based on Terminal Sliding Mode Observer." Ocean Engineering 88 (2014): 426-434. Crossref. Web. https://doi.org/10.1016/j.oceaneng.2014.06.015

Fiorelli, Edward et al. "Multi-AUV Control and Adaptive Sampling in Monterey Bay." IEEE Journal of Oceanic Engineering 31.4 (2006): 935-948. Crossref. Web. https://doi.org/10.1109/JOE.2006.880429

HAN, Min, Nobuhiro OKADA, and Eiji KONDO. "Coordination of an Uncalibrated 3-D Visuo-Motor System Based on Multiple Self-Organizing Maps." JSME International Journal Series C 49.1 (2006): 230-239. Crossref. Web. https://doi.org/10.1299/jsmec.49.230

Huang, Huan, Daqi Zhu, and Feng Ding. "Dynamic Task Assignment and Path Planning for Multi-AUV System in Variable Ocean Current Environment." Journal of Intelligent & Robotic Systems 74.3-4 (2013): 999-1012. Crossref. Web. https://doi.org/10.1007/s10846-013-9870-2

Jan, G.E., Ki Yin Chang, and I. Parberry. "Optimal Path Planning for Mobile Robot Navigation." IEEE/ASME Transactions on Mechatronics 13.4 (2008): 451-460. Crossref. Web. https://doi.org/10.1109/TMECH.2008.2000822

Joordens, Matthew A., and Mo Jamshidi. "Design Of A Prototype Underwater Research Platform For Swarm Robotics." Intelligent Automation & Soft Computing 17.2 (2011): 111-132. Crossref. Web. https://doi.org/10.1080/10798587.2011.10643136

Kang X. D. Oceans, MTS/IEEE Biloxi-Marine Technology for Our Future: Global and Local Challenges, Biloxi

Kulkarni, Indraneel, and Dario Pompili. "Task Allocation for Networked Autonomous Underwater Vehicles in Critical Missions." IEEE Journal on Selected Areas in Communications 28.5 (2010): 716-727. Crossref. Web. https://doi.org/10.1109/JSAC.2010.100609

Kwok, Kwan S. et al. Journal of Intelligent and Robotic Systems 35.1 (2002): 111-122. Crossref. Web. https://doi.org/10.1023/A:1020238115592

Li, H., S.X. Yang, and M.L. Seto. "Neural-Network-Based Path Planning for a Multirobot System With Moving Obstacles." IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews) 39.4 (2009): 410-419. Crossref. Web. https://doi.org/10.1109/TSMCC.2009.2020789

Luo, Chaomin, and Simon X. Yang. "A Bioinspired Neural Network for Real-Time Concurrent Map Building and Complete Coverage Robot Navigation in Unknown Environments." IEEE Transactions on Neural Networks 19.7 (2008): 1279-1298. Crossref. Web. https://doi.org/10.1109/TNN.2008.2000394

Mataric, M.J. "Minimizing Complexity in Controlling a Mobile Robot Population." Proceedings 1992 IEEE International Conference on Robotics and Automation n. pag. Crossref. Web. https://doi.org/10.1109/ROBOT.1992.220192

Millan, Pablo et al. "Formation Control of Autonomous Underwater Vehicles Subject to Communication Delays." IEEE Transactions on Control Systems Technology 22.2 (2014): 770-777. Crossref. Web. https://doi.org/10.1109/TCST.2013.2262768

https://doi.org/10.1109/TNN.2011

Parker L. Heterogeneous multi-robot cooperation

Patino, H.D., R. Carelli, and B.R. Kuchen. "Neural Networks for Advanced Control of Robot Manipulators." IEEE Transactions on Neural Networks 13.2 (2002): 343-354. Crossref. Web. https://doi.org/10.1109/72.991420

Shen, Wei-Min et al. "Hormone-Inspired Self-Organization and Distributed Control of Robotic Swarms." Autonomous Robots 17.1 (2004): 93-105. Crossref. Web. https://doi.org/10.1023/B:AURO.0000032940.08116.f1

Soulignac, Michaël. "Feasible and Optimal Path Planning in Strong Current Fields." IEEE Transactions on Robotics 27.1 (2011): 89-98. Crossref. Web. https://doi.org/10.1109/TRO.2010.2085790

Sun, Bing, Daqi Zhu, and Simon X. Yang. "A BIO-INSPIRED CASCADED APPROACH FOR THREE-DIMENSIONAL TRACKING CONTROL OF UNMANNED UNDERWATER VEHICLES." International Journal of Robotics and Automation 29.4 (2014): n. pag. Crossref. Web. https://doi.org/10.2316/Journal.206.2014.4.206-3891

Yan, Mingzhong, Daqi Zhu, and Simon X. Yang. "A Novel 3-D Bio-Inspired Neural Network Model for the Path Planning of An Auv in Underwater Environments." Intelligent Automation & Soft Computing 19.4 (2013): 555-566. Crossref. Web. https://doi.org/10.1080/10798587.2013.869114

https://doi.org/10.1109/TNN.2003

Yoon, Seokhoon, and Chunming Qiao. "Cooperative Search and Survey Using Autonomous Underwater Vehicles (AUVs)." IEEE Transactions on Parallel and Distributed Systems 22.3 (2011): 364-379. Crossref. Web. https://doi.org/10.1109/TPDS.2010.88

Anmin Zhu, and S.X. Yang. "A Neural Network Approach to Dynamic Task Assignment of Multirobots." IEEE Transactions on Neural Networks 17.5 (2006): 1278-1287. Crossref. Web. https://doi.org/10.1109/TNN.2006.875994

https://doi.org/10.1109/WCICA.2010

Daqi Zhu, Huan Huang, and S. X. Yang. "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 (2013): 504-514. Crossref. Web. https://doi.org/10.1109/TSMCB.2012.2210212

JOURNAL INFORMATION


ISSN PRINT: 1079-8587
ISSN ONLINE: 2326-005X
DOI PREFIX: 10.31209
10.1080/10798587 with T&F
IMPACT FACTOR: 0.652 (2017/2018)
Journal: 1995-Present




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