自主水下机器人（Autonomous Underwater Vehicle，AUV）可以在更深的海域中进行大范围的搜寻，加快深海搜救进度。为了解决AUV在复杂海洋环境下搜索过程中的问题，提高AUV在深海大范围搜寻主动声源过程中的搜寻效率，提出一种基于AUV的主动声源搜寻方法。将AUV搜寻主动声源的过程分为：疑似海域的大范围多目标搜索规划和实时引导搜寻2个阶段。本文主要研究在实时引导搜寻阶段采用基于改进差分进化算法进行动态引导规划，同时考虑引导中避免进入声源声影区导致声源信号丢失的影响，提高AUV声源搜寻效率。通过仿真结果，本文方法与传统的蝗虫优化算法以及粒子群算法对比，规划路径收敛速度更快，路径长度更短，满足AUV在深海大范围搜寻主动声源的搜寻任务效率需求。
Autonomous Underwater Vehicle (AUV) can conduct large-scale searches in deeper waters and speed up the progress of deep-sea search and rescue. In order to solve the problems of AUV in the search process in complex ocean environment and improve the search efficiency of AUV in the process of searching for active sound sources in a wide range of deep sea, an active sound source search method based on AUV is proposed. First, the process of AUV searching for active sound sources is divided into two stages: large-scale multi-target search planning in suspected sea areas and real-time guided search. This paper mainly studies the use of dynamic guidance planning based on the improved differential evolution algorithm in the real-time guidance search phase. At the same time, the impact of avoiding the loss of sound source signals caused by entering the sound shadow area of the sound source during guidance is considered to improve the efficiency of AUV sound source search. Through the simulation results, compared with the traditional locust optimization algorithm and particle swarm algorithm, the planned path converges faster and the path length is shorter, meeting the efficiency requirements of AUV searching for active sound sources in a wide range of deep sea areas.
2024,46(21): 113-118 收稿日期：2024-1-19
DOI：10.3404/j.issn.1672-7649.2024.21.020
分类号：TP24
基金项目：国家重点研发计划资助资助（2022YFC2806000）
作者简介：周仕昊(1998-),男,硕士研究生,研究方向为自主水下机器人技术、人工智能
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