Autonomous underwater vehicles path planning based on autonomous inspired Glasius bio-inspired neural network algorithm

被引：4
|
作者：
Zhu D.-Q. [1 ]
Liu Y. [1 ]
Sun B. [1 ]
Liu Q.-Q. [1 ]
机构：
[1] Laboratory of Underwater Vehicles and Intelligent Systems, Shanghai Maritime University, Shanghai
来源：
Kongzhi Lilun Yu Yingyong/Control Theory and Applications | 2019年 / 36卷 / 02期
基金：
中国国家自然科学基金;
关键词：
Collision avoidance; GBNN; Grid map; Motion planning; Ocean current environment;
D O I：
10.7641/CTA.2018.70576
中图分类号：
学科分类号：
摘要：
Aiming at the path planning problem of autonomous underwater vehicle (AUV) in the complex underwater environment, a novel autonomous inspired algorithm is presented for path planning and obstacle avoidance based on the Glasius biological inspired model and grid map, and the impact of currents is considered. Firstly, the Glasius bio-inspired neural networks (GBNN) model is established, and the model of GBNN is used to represent the working environment of the AUV. Each neuron in the neural network corresponds to the position unit in the grid map. Secondly, according to the distribution of the active output value of neurons in the neural network and the direction reliability algorithm to achieve the autonomic planning AUV motion path. Finally, according to the vector synthesis algorithm to determine the actual direction of AUV navigation. The simulation results show the effectiveness of the biological heuristic model in the path planning of the AUV for the underwater environment with obstacles and ocean current. © 2019, Editorial Department of Control Theory & Applications South China University of Technology. All right reserved.
引用
下载
收藏
页码：183 / 191
页数：8
相关论文
共 20 条
  • [1] Zhu D., Yan M., Survey on technology of mobile robot path planning, Control and Decision, 25, 7, pp. 961-967, (2010)
  • [2] Smith S.L., Jana, Belta C., Et al., Optimal path planning for surveillance with temporal-logic constraints, International Journal of Robotics Research, 30, 14, pp. 1695-1708, (2011)
  • [3] Zhong C., Liu S., Zhang B., Online fast pathplanning based on regionalized spatial knowledge model, Control Theory & Applications, 32, 3, pp. 357-365, (2015)
  • [4] Zhu D., Cao X., Sun B., Et al., Biologically inspired self-organizing map applied to task assignment and path planning of an AUV system, IEEE Transactions on Cognitive and Developmental Systems, 10, 2, pp. 304-313, (2018)
  • [5] Ge S.S., Cui Y.J., Dynamic motion planning for mobile robots using potential field method, Autonomous Robots, 13, 3, pp. 207-222, (2002)
  • [6] Sabattini L., Secchi C., Fanytuzzi C., Arbitrarily shaped formations of mobile robots: artificial potential fields and coordinate transformation, Autonomous Robots, 30, 4, pp. 385-397, (2011)
  • [7] Fukuda T., Ito S., Arai F., Et al., Navigation system based on ceiling landmark recognition for autonomous mobile robot-landmark detection based on fuzzy template matching (FTM), Proceedings of the 1995 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 150-155, (1995)
  • [8] Wulfmeier M., Rao D., Wang D.Z., Et al., Large-scale cost function learning for path planning using deep inverse reinforcement learning, International Journal of Robotics Research, 36, 10, pp. 1073-1087, (2017)
  • [9] Vasudevan C., Ganesan K., Case-based path planning for autonomous underwater vehicles, IEEE Internation Symposium on Intelligent Control, pp. 160-165, (1994)
  • [10] Shang Y., Xu Y., Pang Y., Autonomous underwater vehicles global path planning using case-based leaning algorithm, Robot, 20, 6, pp. 427-432, (1998)
12