论文标题
在洋流的影响下，以生物为基础的神经网络为UUV的最佳路径计划
Bio-inspired Neural Network-based Optimal Path Planning for UUVs under the Effect of Ocean Currents
论文作者
论文摘要
为了消除水下环境中最佳路径的影响，本文提出了一种专为无人水下汽车（UUV）设计的智能算法。该算法由两个部分组成：一种基于神经网络的算法，该算法会扣除最短路径并避免所有可能的碰撞；以及通过洋流的效果带来的调整组件，平衡偏离偏差。提出的算法的优化结果详细介绍，并与不考虑电流效果的路径计划算法进行了比较。比较结果证明了遇到不同方向和速度的电流时路径计划方法的有效性。
To eliminate the effect of ocean currents when addressing the optimal path in the underwater environment, an intelligent algorithm designed for the unmanned underwater vehicle (UUV) is proposed in this paper. The algorithm consists of two parts: a neural network-based algorithm that deducts the shortest path and avoids all possible collisions; and an adjusting component that balances off the deviation brought by the effect of ocean currents. The optimization results of the proposed algorithm are presented in detail, and compared with the path planning algorithm that does not consider the effect of currents. Results of the comparison prove the effectiveness of the path planning method when encountering currents of different directions and velocities.
