Flow recognition of underwater vehicle based on the perception mechanism of lateral line

被引：0
作者：
Wu N. [1 ,2 ]
Wu C. [1 ,2 ]
Ge T. [1 ,2 ]
Wang T. [1 ,2 ]
Zhang Y. [1 ,2 ]
机构：
[1] School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai
[2] State Key Laboratory of Ocean Engineering, Shanghai Jiao Tong University, Shanghai
来源：
Jixie Gongcheng Xuebao/Journal of Mechanical Engineering | 2016年 / 52卷 / 13期
关键词：
Flow perception; Lateral line; Linear discriminant analysis; Support vector machine; Unmanned undersea vehicle;
D O I：
10.3901/JME.2016.13.054
中图分类号：
学科分类号：
摘要：
Unmanned undersea vehicle (UUV) is a tool widely used in underwater detection. It is the crucial factor for UUV to work well in deep underwater environment. With a view to the problems of the capability of robot to percept the underwater situation compared with fish, an model that can identify the flow characteristic of the water is presented. The control of robot must consume large amounts of energy to maintain the position and status traditionally, which is passive and dangerous for UUV to cope with the challenge in the water. Therefore, it is very important for underwater vehicles to recognize current and explore ocean further. Studies on the lateral line system show that it can distinguish the flow of water and improve the motion of the fish. Aimed at a torpedo UUV, the mechanism and capability of lateral line system is studied and realized using the pressure data of vehicle calculated by CFD software. The linear discriminant analysis and support vector machine methods are selected to establish the flow sensing classification model. The tests demonstrate that the method can identify different flows around the vehicle effectively and can provide a new view for the study of underwater vehicle during the development and use of ocean. © 2016 Journal of Mechanical Engineering.
引用
收藏
页码：54 / 59
页数：5
相关论文
共 13 条
  • [1] Verschure P.F.M.J., Voegtliin T., Douglas R.J., Environmentally mediated synergy between perception and behaviour in mobile robots, Nature, 425, 6958, pp. 620-624, (2003)
  • [2] Abdulsadda A., Tan X.B., Underwater tracking of a moving dipole source using an artificial lateral line: Algorithm and experimental validation with ionic polymer-metal composite flow sensors, Smart Materials and Structures, 22, 4, pp. 1-19, (2013)
  • [3] Ren Z., Mohseni K., A model of the lateral line of fish for vortex sensing, Bioinspiration & Biomimetics, 7, 3, pp. 036016-036029, (2012)
  • [4] Fan Z.F., Chen J., Zou J., Et al., Design and fabrication of artificial lateral line flow sensors, Journal of Micromechanics and Microengineering, 12, 5, pp. 655-661, (2002)
  • [5] Li J.H., Lee P.M., Design of an adaptive nonlinear controller for depth control of an autonomous underwater vehicle, Ocean Engineering, 32, 17-18, pp. 2165-2181, (2005)
  • [6] Jun B.H., Park J.Y., Lee F.Y., Et al., Development of the AUV 'ISiMI' and a free running test in an ocean engineering basin, Ocean Engineering, 36, 1, pp. 2-14, (2009)
  • [7] Wu N., Liu G., Li S., Et al., Thrustor control of autonomous underwater vehichle based on artificial immune feedback, Journal of Mechanical Engineering, 47, 21, pp. 22-27, (2011)
  • [8] Zhang H., Xu Y.R., Cai H.P., Using CFD software to calculate hydrodynamic coefficients, Journal of Marine Science and Application, 9, 2, pp. 149-155, (2010)
  • [9] Yuan W., Liu G., Zhu S., Identification method of hydrodynamic parameters of autonomous underwater vehicle based on genetic algorithm, Journal of Mechanical Engineering, 46, 11, pp. 96-99, (2010)
  • [10] Xu F., Zou Z.J., Yin J.C., Et al., Identification modeling of underwater vehicles' nonlinear dynamics based on support vector machines, Ocean Engineering, 67, pp. 68-76, (2013)
12