Following the development of autonomous underwater vehicles (AUVs), multiple trajectory-based submarine target information collection constitutes one of the key technologies that significantly influence underwater information collection ability and deployment efficiency. In this paper, we propose an underwater information collection AUV, O-AUV, that can perceive the omnidirectional area and could detect a larger area than the traditional AUV. A 3D sensing model for the O-AUV is proposed to describe the complex underwater information collection spaces. Thereafter, a cube-based environment model involving candidate observation point calculation methods are suggested to adapt the O-AUV model. A voyage cost map is also built according to the multi-AUV path planning for a common submarine mission that must traverse numerous mission targets in complex environments through the R-Dijkstra algorithm. Specifically, the voyage planning problem is solved through a critical algorithm called ANSGA (accelerated NSGA-II algorithm), which in turn, is developed by modifying the non-dominated sorting genetic algorithm (NSGA-II) to accelerate the optimization rate for the Pareto solution. Experiments are carried out in MATLAB, and the results verify the validity of the proposed O-AUV+ANSGA algorithm framework.

中文翻译：

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基于全方位感知能力的多AUV路径规划系统

随着自主式水下航行器（AUV）的发展，基于多轨迹的潜艇目标信息收集构成了对水下信息收集能力和部署效率产生重大影响的关键技术之一。在本文中，我们提出了一种水下信息采集 AUV，O-AUV，它可以感知全方位区域，并且可以检测到比传统 AUV 更大的区域。提出了一种用于 O-AUV 的 3D 传感模型来描述复杂的水下信息收集空间。此后，建议使用包含候选观测点计算方法的基于立方体的环境模型来适应 O-AUV 模型。还根据多AUV路径规划构建了航行成本图，用于通过R-Dijkstra算法穿越复杂环境中的众多任务目标的普通潜艇任务。具体来说，航次规划问题是通过一种称为ANSGA（加速NSGA-II算法）的关键算法来解决的，该算法又是通过修改非支配排序遗传算法（NSGA-II）来加速Pareto的优化速度解决方案。在MATLAB中进行了实验，结果验证了所提出的O-AUV+ANSGA算法框架的有效性。是通过修改非支配排序遗传算法（NSGA-II）开发的，以加快帕累托解的优化速度。在MATLAB中进行了实验，结果验证了所提出的O-AUV+ANSGA算法框架的有效性。是通过修改非支配排序遗传算法（NSGA-II）开发的，以加快帕累托解的优化速度。在MATLAB中进行了实验，结果验证了所提出的O-AUV+ANSGA算法框架的有效性。