The paper presents an innovative approach (CBNNTAP) that addresses the
complexities and challenges introduced by ocean currents when optimizing target
assignment and motion planning for a multi-unmanned underwater vehicle (UUV)
system. The core of the proposed algorithm involves the integration of several
key components. Firstly, it incorporates a bio-inspired neural network-based
(BINN) approach which predicts the most efficient paths for individual UUVs
while simultaneously ensuring collision avoidance among the vehicles. Secondly,
an efficient target assignment component is integrated by considering the path
distances determined by the BINN algorithm. In addition, a critical innovation
within the CBNNTAP algorithm is its capacity to address the disruptive effects
of ocean currents, where an adjustment component is seamlessly integrated to
counteract the deviations caused by these currents, which enhances the accuracy
of both motion planning and target assignment for the UUVs. The effectiveness
of the CBNNTAP algorithm is demonstrated through comprehensive simulation
results and the outcomes underscore the superiority of the developed algorithm
in nullifying the effects of static and dynamic ocean currents in 2D and 3D
scenarios.

该论文介绍了一种创新的方法（CBNNTAP），解决了多无人水下航行器系统在优化目标分配和运动规划时由海洋洋流引入的复杂性和挑战。该算法集成了生物启发的神经网络、有效的目标分配和适应海洋洋流影响的调整组件，通过综合模拟结果证明了其优越性。