Complete Coverage Autonomous Underwater Vehicles Path Planning Based on Glasius Bio-Inspired Neural Network Algorithm for Discrete and Centralized Programming
Complete Coverage Autonomous Underwater Vehicles Path Planning Based on Glasius Bio-Inspired Neural Network Algorithm for Discrete and Centralized Programming
复制标题
基于Glasius仿生神经网络离散集中编程算法的全覆盖自主水下航行器路径规划
DOI：
10.1109/tcds.2018.2810235
复制
发表时间：
2019-03-01
影响因子：
5
通讯作者：
Luo, Chaomin
中科院分区：
文献类型：
--
作者：
Sun, Bing;Zhu, Daqi;Luo, Chaomin
对于自动驾驶水下车辆（AUV）的完整覆盖路径计划，提出了具有离散和集中编程的Glasius生物启发的神经网络（GBNN）算法的新策略。首先讨论基于网格图和神经网络的多AUV的基本建模。然后，基于GBNN算法引入单个AUV完整覆盖的设计，该算法是一种新开发的工具，具有少量计算和高效率。为了解决大型水范围的单个AUV完全覆盖任务的难度，根据GBNN算法提出了多AUV全覆盖离散和集中编程。进行仿真实验是为了确认通过拟议的算法，多AUV可以计划合理且无碰撞的覆盖路径，并通过劳动与合作划分在同一任务领域完全覆盖。
For the complete coverage path planning of autonomous underwater vehicles (AUVs), a new strategy with Glasius bio-inspired neural network (GBNN) algorithm with discrete and centralized programming is proposed. The basic modeling for multi-AUVs complete coverage problem based on grid map and neural network is discussed first. Then, the design for single AUV complete coverage is introduced based on GBNN algorithm which is a new developed tool with small amount of calculation and high efficiency. In order to solve the difficulty of single AUV full coverage task of large water range, the multi-AUV full coverage discrete and centralized programming is proposed based on GBNN algorithm. The simulation experiment is conducted to confirm that through the proposed algorithm, multi-AUVs can plan reasonable and collision-free coverage path and reach full coverage on the same task area with division of labor and cooperation.