Multiple autonomous underwater vehicles (AUVs) are popular for challenging submarine missions. In this article, we focus on the multi-AUV path planning for a common class of missions that need to traverse lots of mission targets in large and complex environments. Given that AUVs are often launched from a movable surface vehicle, e.g., a ship, a multi-AUV target traveling problem is formulated with a requirement of surface point location. Thereafter, a hybrid metaheuristic approach is developed by sequentially performing a cube-based environment modeling, cost map building, voyage planning, and detailed trajectory planning. Specifically, the shortest path faster algorithm (SPFA) is adopted to build a cost map among targets and candidate surface points, and the A* search is utilized for trajectory planning. The optimality of both SPFA and A* can indeed be guaranteed. Thus, voyage planning becomes critical and an algorithm namely DE-C-ACO is proposed by combining the ant colony optimization (ACO) and the differential evolution (DE) with a cluster-based adjustment strategy, i.e., DE-C. DE-C and ACO evolve in parallel for surface point location and voyage generation, respectively. Experiments based on realistic bathymetries are conducted and the results validate the effectiveness and efficiency of the proposed DE-C-ACO.

中文翻译：

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用于困难目标旅行任务的多 AUV 系统中的路径规划：混合元启发式方法

多个自主水下航行器 (AUV) 在具有挑战性的潜艇任务中很受欢迎。在本文中，我们专注于需要在大型复杂环境中穿越大量任务目标的常见任务类别的多 AUV 路径规划。鉴于 AUV 通常是从可移动的水面车辆（例如船）发射，多 AUV 目标行进问题被公式化为具有表面点位置的要求。此后，通过依次执行基于立方体的环境建模、成本地图构建、航行规划和详细轨迹规划，开发了混合元启发式方法。具体而言，采用最短路径更快算法（SPFA）在目标和候选表面点之间构建成本图，并利用A*搜索进行轨迹规划。确实可以保证SPFA和A*的最优性。因此，航程规划变得至关重要，通过将蚁群优化（ACO）和差分进化（DE）与基于集群的调整策略（即 DE-C）相结合，提出了一种称为 DE-C-ACO 的算法。DE-C 和 ACO 分别在表面点定位和航次生成方面并行发展。进行了基于实际测深的实验，结果验证了所提出的 DE-C-ACO 的有效性和效率。