Full Text:   <320>

Summary:  <186>

CLC number: TP249

On-line Access: 2019-04-09

Received: 2018-09-18

Revision Accepted: 2019-02-01

Crosschecked: 2019-03-14

Cited: 0

Clicked: 2121

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Da-qi Zhu

http://orcid.org/0000-0001-7252-4952

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Frontiers of Information Technology & Electronic Engineering  2019 Vol.20 No.3 P.330-341

http://doi.org/10.1631/FITEE.1800562


Multi-AUV SOM task allocation algorithm considering initial orientation and ocean current environment


Author(s):  Da-qi Zhu, Yun Qu, Simon X. Yang

Affiliation(s):  Laboratory of Underwater Vehicles and Intelligent Systems, Shanghai Maritime University, Shanghai 201306, China; more

Corresponding email(s):   zdq367@aliyun.com, syang@uoguelph.ca

Key Words:  Autonomous underwater vehicles, Self-organizing neural networks, Azimuths, Ocean current


Da-qi Zhu, Yun Qu, Simon X. Yang. Multi-AUV SOM task allocation algorithm considering initial orientation and ocean current environment[J]. Frontiers of Information Technology & Electronic Engineering, 2019, 20(3): 330-341.

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Abstract: 
There is an ocean current in the actual underwater working environment. An improved self-organizing neural network task allocation model of multiple autonomous underwater vehicles (AUVs) is proposed for a three-dimensional underwater workspace in the ocean current. Each AUV in the model will be competed, and the shortest path under an ocean current and different azimuths will be selected for task assignment and path planning while guaranteeing the least total consumption. First, the initial position and orientation of each AUV are determined. The velocity and azimuths of the constant ocean current are determined. Then the AUV task assignment problem in the constant ocean current environment is considered. The AUV that has the shortest path is selected for task assignment and path planning. Finally, to prove the effectiveness of the proposed method, simulation results are given.

基于AUV初始方向角和海流环境的SOM任务分配算法

摘要:实际水下环境存在海流。本文针对多自治机器人任务分配系统提出一个改进的自组织神经网络算法。该算法充分考虑自治水下机器人初始方向角和海流环境。每个自治水下机器人都参与竞争。选出实际航行路径最短的自治水下机器人作为获胜神经元,同时确保总航行路径最短。首先,初始化每个自治水下机器人的位置与方向角以及海流流速与方向。其次,通过竞争,选择海流环境下最短航行路径的水下机器人作为获胜神经元,并将该获胜神经元分配给相应目标点。为证明该算法有效性,给出相应仿真结果。

关键词:自治水下机器人;自组织神经网络;初始方向角;海流

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

Reference

[1]Akkiraju R, Keskinocak P, Murthy S, et al., 2001. An agent-based approach for scheduling multiple machines. Appl Intell, 14(2):135-144.

[2]Cao X, Zhu DQ, 2015. Multi-AUV task assignment and path planning with ocean current based on biological inspired self-organizing map and velocity synthesis algorithm. Intell Autom Soft Comput, 23(1):31-39.

[3]Elango M, Nachiappan S, Tiwari MK, 2011. Balancing task allocation in multi-robot systems using k-means clustering and auction based mechanisms. Exp Syst Appl, 38(6): 6486-6491.

[4]Erol M, Vieira LFM, Gerla M, 2007. AUV-aided localization for underwater sensor networks. Proc Int Conf on Wireless Algorithms, Systems and Applications, p.44-54.

[5]Huang H, Zhu DQ, Yuan F, 2012. Dynamic task assignment and path planning for multi-AUV system in 2D variable ocean current environment. Proc 24th IEEE Chinese Control and Decision Conf, p.3660-3664.

[6]Huang H, Zhu DQ, Ding F, 2014. Dynamic task assignment and path planning for multi-AUV system in variable ocean current environment. J Intell Rob Syst, 74(3-4): 999-1012.

[7]Kohonen T, Kaski S, Lagus K, et al., 2000. Self organization of a massive document collection. IEEE Trans Neur Netw, 11(3):574-585.

[8]Nouri NM, Zeinali M, Jahangardy Y, 2016. AUV hull shape design based on desired pressure distribution. J Mar Sci Technol, 21(2):203-215.

[9]Paull L, Saeedi S, Seto M, et al., 2014. AUV navigation and localization: a review. IEEE J Ocean Eng, 39(1):131-149.

[10]Redfield S, 2013. Cooperation between underwater vehicles. In: Seto ML (Ed.), Marine Robot Autonomy. Springer, New York, p.257-286.

[11]Smith RN, Cooksey P, Py F, et al., 2014. Adaptive path planning for tracking ocean fronts with an autonomous underwater vehicle. Proc 14th Int Symp on Experimental Robotics, p.761-775.

[12]Sotzing CC, Lane DM, 2010. Improving the coordination efficiency of limited-communication multi-autonomus underwater vehicle operations using a multiagent architecture. J Field Rob, 27(4):412-429.

[13]Sujit PB, Beard R, 2007. Distributed sequential auctions for multiple UAV task allocation. Proc American Control Conf, p.3955-3960.

[14]Sujit PB, Sinha A, Ghose D, 2005. Multi-UAV task allocation using team theory. Proc 44th IEEE Conf on Decision and Control, p.1497-1502.

[15]Wang Z, Feng XN, 2011. A cooperative simulation system for AUV based on multi-agent. Proc Int Conf on Virtual Reality and Visualization, p.109-114.

[16]Yu L, Zhu DQ, 2017. Task assignment and path planning of AUV system based on Glasius bio-inspired self- organizing map neural network algorithm. Syst Simul Technol, 13(3):230-234, 240 (in Chinese).

[17]Zadeh SM, Powers DMW, Yazdani AM, 2016. A novel efficient task-assign route planning method for AUV guidance in a dynamic cluttered environment. https://arxiv.org/abs/1604.02524]

[18]Zhu A, Yang SX, 2006. A neural network approach to dynamic task assignment of multirobots. IEEE Trans Neur Netw, 17(5):1278-1287.

[19]Zhu DQ, Li X, Yan M, 2012. Task assignment algorithm of multi-AUV based on self-organizing map. Contr Dec, 27(8):1201-1205, 1210.

[20]Zhu DQ, Huang H, Yang SX, 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 Trans Cybern, 43(2):504-514.

[21]Zhu DQ, Cao X, Sun B, et al., 2018a. Biologically inspired self-organizing map applied to task assignment and path planning of an AUV system. IEEE Trans Cogn Dev Syst, 10(2):304-313.

[22]Zhu DQ, Tian C, Sun B, et al., 2018b. Complete coverage path planning of autonomous underwater vehicle based on GBNN algorithm. J Intell Rob Syst, 94(1):237–249.

[23]Zhu DQ, Liu Y, Sun B, 2018c. Task assignment and path planning of a multi-AUV system based on a Glasius bio-inspired self-organising map algorithm. J Navig, 71(2): 482-496.

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