Autonomous path planning plays an important role in the navigation of intelligent underwater robots. Path planning is a nondeterministic polynomial hard issue in classical path planning models. This problem can be solved using various sample-based strategies. However, the effectiveness of these sample-based strategies is significantly lower in underwater environments, owing to the special undulating terrain and obstacles that are sparser compared to those in the ground. In this study, a more efficient underwater path planning method is proposed for underwater robot navigation. The method employs a goal-biased Gaussian sampling algorithm to select searching nodes optimally, and a focused optimal search algorithm is proposed to accelerate the path optimization process. Combining these two algorithms results in high-efficiency and fast autonomous underwater path planning. Experimental results demonstrate that our method can generate a shorter path and is more efficient than a rapidly exploring random tree star in underwater robot navigation.

自主路径规划在智能水下机器人的导航中发挥着重要作用。路径规划是经典路径规划模型中的非确定性多项式难题。这个问题可以使用各种基于样本的策略来解决。然而，这些基于样本的策略在水下环境中的有效性明显较低，这是由于特殊的起伏地形和障碍物比地面更稀疏。在这项研究中，提出了一种更有效的水下路径规划方法用于水下机器人导航。该方法采用目标偏向高斯采样算法来最优选择搜索节点，并提出一种聚焦最优搜索算法来加速路径优化过程。结合这两种算法可以实现高效、快速的自主水下路径规划。实验结果表明，在水下机器人导航中，我们的方法可以生成更短的路径，并且比快速探索的随机树星更有效。