As a new type of marine unmanned intelligent equipment, autonomous underwater vehicle (AUV) has been widely used in the field of ocean observation, maritime rescue, mine countermeasures, intelligence reconnaissance, etc. Especially in the underwater search mission, the technical advantages of AUV are particularly obvious. However, limited operational capability and sophisticated mission environments are also difficulties faced by AUV. To make better use of AUV in the search mission, we establish the DMACSS (distributed multi-AUVs collaborative search system) and propose the ACSLA (autonomous collaborative search learning algorithm) integrated into the DMACSS. Compared with the previous system, DMACSS adopts a distributed control structure to improve the system robustness and combines an information fusion mechanism and a time stamp mechanism, making each AUV in the system able to exchange and fuse information during the mission. ACSLA is an adaptive learning algorithm trained by the RL (Reinforcement learning) method with a tailored design of state information, reward function, and training framework, which can give the system optimal search path in real-time according to the environment. We test DMACSS and ACSLA in the simulation test. The test results demonstrate that the DMACSS runs stably, the search accuracy and efficiency of ACSLA outperform other search methods, thus better realizing the cooperation between AUVs, making the DMACSS find the target more accurately and faster.

中文翻译：

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基于自主协同搜索学习算法的多AUV协同目标搜索

自主水下航行器（AUV）作为一种新型的海洋无人驾驶智能设备，已广泛用于海洋观测，海上救援，地雷对策，情报侦察等领域。特别是在水下搜索任务中，AUV的技术优势特别明显。但是，有限的作战能力和复杂的任务环境也是AUV所面临的困难。为了在搜索任务中更好地利用AUV，我们建立了DMACSS（分布式多AUV协作搜索系统），并提出了集成到DMACSS中的ACSLA（自主协作搜索学习算法）。与以前的系统相比，DMACSS采用分布式控制结构来提高系统的鲁棒性，并结合了信息融合机制和时间戳机制，使系统中的每个AUV都能在执行任务时交换和融合信息。ACSLA是一种通过RL（强化学习）方法训练的自适应学习算法，具有针对性的状态信息，奖励功能和训练框架的设计，可以根据环境实时提供系统最佳的搜索路径。我们在模拟测试中测试DMACSS和ACSLA。测试结果表明，DMACSS运行稳定，ACSLA的搜索精度和效率优于其他搜索方法，从而更好地实现了AUV的协作，使DMACSS能够更准确，更快地找到目标。ACSLA是一种通过RL（强化学习）方法训练的自适应学习算法，具有针对性的状态信息，奖励功能和训练框架的设计，可以根据环境实时提供系统最佳的搜索路径。我们在模拟测试中测试DMACSS和ACSLA。测试结果表明，DMACSS运行稳定，ACSLA的搜索精度和效率优于其他搜索方法，从而更好地实现了AUV的协作，使DMACSS能够更准确，更快地找到目标。ACSLA是一种通过RL（强化学习）方法训练的自适应学习算法，具有针对性的状态信息，奖励功能和训练框架的设计，可以根据环境实时提供系统最佳的搜索路径。我们在模拟测试中测试DMACSS和ACSLA。测试结果表明，DMACSS运行稳定，ACSLA的搜索精度和效率优于其他搜索方法，从而更好地实现了AUV的协作，使DMACSS能够更准确，更快地找到目标。