Danjie Zhu, Ya-Jun Pan, and Simon X. Yang
Bio-inspired neural network, motion planning, ocean current effect, task assignment, unmanned underwater vehicle (UUV)
A novel algorithm based on a bio-inspired neural network (BINN) is proposed to address the challenges posed by ocean currents in achieving optimal task assignment and motion planning for a system of multiple unmanned underwater vehicles (UUVs). The algorithm comprises three main components: a BINN for predicting the optimal path and avoiding collisions among the UUVs; an adjustment component that compensates for the deviations generated by ocean currents and calculates energy consumption; and an optimal task assignment component based on motion planning costs, with the distance calculated by the neural network and the energy consumption involved. The simulation results of the proposed algorithm are showcased and contrasted with an algorithm that neglects the influence of ocean currents. This comparative analysis serves to illustrate the effectiveness of the method under conditions of both static and dynamic currents.
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