A Multi-AUV Searching Algorithm Based on Neuron Network with Obstacle

Abstract

In this paper, a region search algorithm based on bio-inspired neural network is proposed, which can be used for AUVs to perform target search tasks in underwater regions with obstacles. Compared to neuron cells, the search area is divided into several discrete sub-areas. Adjacent neurons have synaptic connections and can transmit excitatory action. In order to avoid collisions during the search process, the obstacles and AUVs involved in search tasks are introduced as inhibitory sources of excitation into the neural network by constructing a neuronal excitation delivery model. By constructing hypothetical targets and introducing them into the neural network as stimulating sources of excitation, the AUVs are guided to quickly search for areas where the target is likely to exist, thereby they can efficiently completing the search tasks. Finally, the corresponding simulation results are given in order to prove the effectiveness of the search algorithm.