Path planning and task assignment of the multi-AUVs system based on the hybrid bio-inspired SOM algorithm with neural wave structure

Abstract

A hybrid bio-inspired self-organizing map neural network algorithm is proposed for path planning and task assignment for a multi-autonomous underwater vehicle (AUV) system within a mixed (dynamic and static) three-dimensional (3D) environment. A 3D hybrid bio-inspired neural network model is established to represent the underwater environment and the distribution of the neuron pheromone content gradually diffusing, centered on the source point of the neural wave. Through self-regulation of the neural wave diffusion, the targets can achieve self-adaptive capabilities. “Multiple Newton interpolation” is used to identify the real target among interference targets, and the multi-AUV system transitions from tracking the false target to tracking the real target. Based on the principle of AUV individual kinematics, a velocity vector synthesis algorithm is proposed to overcome the interference of ocean currents. Simulation studies performed in five different environments demonstrate that the proposed algorithm has high adaptability, and the potential for wide application because its neural waves can be updated in real time.