Task Assignment and Path Planning of a Multi-AUV System Based on a Glasius Bio-Inspired Self-Organising Map Algorithm
Task Assignment and Path Planning of a Multi-AUV System Based on a Glasius Bio-Inspired Self-Organising Map Algorithm
复制标题
基于 Glasius 仿生自组织地图算法的多 AUV 系统任务分配和路径规划
DOI：
10.1017/s0373463317000728
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发表时间：
2018
影响因子：
2.4
通讯作者：
Sun Bing
中科院分区：
文献类型：
--
作者：
Zhu Daqi;Liu Yu;Sun Bing
针对多自主水下机器人(MULTI-AUV)系统任务分配和路径规划问题，提出了一种新的Glasius生物启发自组织映射(GBSOM)神经网络算法来解决三维网格地图中的相关问题。首先，建立了一个三维Glasius生物启发神经网络(GBNN)模型来表示三维水下作业环境。使用该模型，计算了GBNN中每个节点的神经活动强度。其次，使用自组织映射(SOM)神经网络将目标分配给一组AUV，并确定AUV访问目标点的顺序。最后，在任务分配完成后，根据GBNN中神经元活动的大小，自主规划下一个AUV目标点。通过重复上述三个步骤，就完成了对所有目标点的访问。仿真和对比研究表明，该算法可以克服SOM算法的速度跳跃问题和三维水下静态或动态障碍物环境下的路径规划问题。
For multi-Autonomous Underwater Vehicle (multi-AUV) system task assignment and path planning, a novel Glasius Bio-inspired Self-Organising Map (GBSOM) neural networks algorithm is proposed to solve relevant problems in a Three-Dimensional (3D) grid map. Firstly, a 3D Glasius Bio-inspired Neural Network (GBNN) model is established to represent the 3D underwater working environment. Using this model, the strength of neural activity is calculated at each node within the GBNN. Secondly, a Self-Organising Map (SOM) neural network is used to assign the targets to a set of AUVs and determine the order of the AUVs to access the target point. Finally, according to the magnitude of the neuron activity in the GBNN, the next AUV target point can be autonomously planned when the task assignment is completed. By repeating the above three steps, access to all target points is completed. Simulation and comparison studies are presented to demonstrate that the proposed algorithm can overcome the speed jump problem of SOM algorithms and path planning in the 3D underwater environments with static or dynamic obstacles.