Multi-AUV SOM task allocation algorithm considering initial orientation and ocean current environment
- 63 Downloads
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.
Key wordsAutonomous underwater vehicles Self-organizing neural networks Azimuths Ocean current
Unable to display preview. Download preview PDF.
We would like to thank Dr. Bing SUN for discussion.
- 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. https://doi.org/10.1109/WASA.2007.34
- 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. https://doi.org/10.1109/CCDC.2012.6243093
- 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. https://doi.org/10.1007/978-3-319-23778-7_50
- Sujit PB, Beard R, 2007. Distributed sequential auctions for multiple UAV task allocation. Proc American Control Conf, p.3955–3960. https://doi.org/10.1109/ACC.2007.4282558
- 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. https://doi.org/10.1109/CDC.2005.1582370
- 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. https://doi.org/10.1109/ICVRV.2011.48
- 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).Google Scholar
- Zadeh SM, Powers DMW, Yazdani AM, 2016. A novel efficient task-assign route planning method for AUV guidance in a dynamic cluttered environment. https://doi.org/arxiv.org/abs/1604.02524
- 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.Google Scholar
- 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. https://doi.org/10.1109/TSMCB.2012.2210212 CrossRefGoogle Scholar