AUV cooperative hunting algorithm based on bio-inspired neural network for path conflict state

Cooperative hunting is a challenging and critical issue in multi-AUV system research. To conduct the cooperative hunting by multi-AUV in underwater environments, the AUVs not only need to take into account catch the target efficiently, but also need to avoid path conflict. In this paper, a novel algorithm based on bio-inspired neural network is proposed for the cooperative hunting by multi-AUV. Firstly, based on the establishment of bio-inspired neural network model, AUV working environment is represent by it, there is one-to-one correspondence between each neuron in neural network and the position of the grid map of underwater environment. Then the activity values of biological neurons guide the AUV´s sailing path and finally the target is surrounded by AUVs. In addition, a method called location forecasting is used to solve the path conflict of AUVs. The simulation results show that the algorithm used in the paper can provide a rapid and high efficient hunting in the underwater environment with obstacles and non-obstacles.