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。

多个无人水下航行器系统的无干扰优化目标分配和运动规划

Current Effect-eliminated Optimal Target Assignment and Motion Planning  for a Multi-UUV System

In this paper, we study the one-shot and lifelong versions of the Target
Assignment and Path Finding problem in automated sortation centers, where each
agent needs to constantly assign itself a sorting station, move to its assigned
station without colliding with obstacles or other agents, wait in the queue of
that station to obtain a parcel for delivery, and then deliver the parcel to a
sorting bin. The throughput of such centers is largely determined by the total
idle time of all stations since their queues can frequently become empty. To
address this problem, we first formalize and study the one-shot version that
assigns stations to a set of agents and finds collision-free paths for the
agents to their assigned stations. We present efficient algorithms for this
task based on a novel min-cost max-flow formulation that minimizes the total
idle time of all stations in a fixed time window. We then demonstrate how our
algorithms for solving the one-shot problem can be applied to solving the
lifelong problem as well. Experimentally, we believe to be the first
researchers to consider real-world automated sortation centers using an
industrial simulator with realistic data and a kinodynamic model of real
robots. On this simulator, we showcase the benefits of our algorithms by
demonstrating their efficiency and effectiveness for up to 350 agents.

研究了自动分拣中心中一次分配分拣站点和路径规划问题，并提出了基于最小成本最大流形式化算法的有效解决方案。该方案不仅适用于一次性问题，还可用于长期问题。在真实数据和运动学模型的工业模拟器上进行的实验表明了该方案的高效性和有效性。