摘要
随着海洋环境复杂性和现代战争需求的提升,无人艇在军事、科研和商业领域的应用不断扩大,尤其在海上作战中展现出精准打击的重要价值。本文研究无人艇在复杂海上环境中的目标打击性能,聚焦路径规划优化、打击精度提升和作战协同方式。基于遗传算法(Genetic Algorithm,GA)、粒子群优化(Particle Swarm Optimization,PSO)和强化学习(Reinforcement Learning,RL),提出路径规划优化方案,并分析无人艇在动态环境适应、通信、自主性与智能化等方面的技术挑战。最后,展望无人艇在高效路径规划、协同作战及跨领域应用中的未来发展方向,为提升其作战能力提供理论支持。
With the increasing complexity of marine environments and the demands of modern warfare,unmanned surface vehicles(USVs)are seeing expanded applications in military,research,and commercial fields,particularly demonstrating significant value in precision strikes during maritime operations.This paper investigates the target-strike performance of USVs in complex marine environments,focusing on path planning optimization,strike accuracy enhancement,and collaborative combat strategies.It proposes path planning solutions based on genetic algorithm(GA),particle swarm optimization(PSO),and reinforcement learning(RL),while analyzing technical challenges such as dynamic environment adaptation,communication issues,autonomy,and intelligence.Furthermore,the paper explores future developments in efficient path planning,collaborative operations,and cross-domain applications,providing theoretical support to enhance USV combat capabilities.
作者
陈硕
CHEN Shuo(Noncommissioned Officer Academy of PAP,Hangzhou,Zhejiang 311400,China)
出处
《新一代信息技术》
2024年第4期25-29,共5页
New Generation of Information Technology
关键词
无人艇
目标打击任务
路径规划
智能化决策
unmanned surface vehicles(USVs)
target strike missions
path planning
intelligent decision-making
