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首页> 外文期刊>Ocean Engineering >Coordinated path planning for an unmanned aerial-aquatic vehicle (UAAV) and an autonomous underwater vehicle (AUV) in an underwater target strike mission
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Coordinated path planning for an unmanned aerial-aquatic vehicle (UAAV) and an autonomous underwater vehicle (AUV) in an underwater target strike mission

机译:在水下目标罢工任务中的无人机水产型车辆(UAAV)和自主水下车辆(AUV)的协调路径规划

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摘要

Unmanned system has become more and more popular as it can adapt to diverse environments and has prospective applications. Especially, the coordination among heterogeneous vehicles is capable of completing complicated tasks, which is often beyond the ability of homogeneous vehicles. In this paper, the underwater target strike mission is concentrated, and the mission is completed by the coordination between a UAAV and an AUV. UAAV and AUV are deployed in this mission because UAAV has strong search ability in air and can communicate with AUV directly after it dives into water. Firstly, to decompose the problem, the mission is divided into two phases, i.e., single flying of UAAV and underwater coordination between UAAV and AUV. In the coordinated path planning model, the motion of vehicles, the constraints in different media and the optimization index in each phase are all formulated into mathematical forms. Based on the particle swarm optimization (PSO) algorithm, the collocation points are used to determine the locations of control variables. Those points can reduce the computation load and improve the solution quality, and they are distributed by height and moment according to the forms of constraints in each phase. Besides, the strategy of addressing infeasible solutions is generated to guarantee the normal operation of PSO-based algorithm. Simulation results demonstrate that the proposed two-phase coordinated path planning method can generate coordinated paths, and the obtained results is very close to the optimal solution in theory. Compared to the whole method, the two-phase method can better deal with the complicated constraints in each phase.
机译:无人驾驶系统变得越来越受欢迎,因为它可以适应不同的环境,并且具有潜在应用程序。特别是,异构车辆之间的协调能够完成复杂的任务,这些任务通常超出均匀车辆的能力。在本文中,集中了水下目标罢工使命,使命由UAAV与AUV之间的协调完成。 UAAV和AUV部署在本任务中,因为UAAV在空气中具有很强的搜索能力,并且可以在它潜入水中直接与AUV通信。首先,为了解决问题,使命分为两个阶段,即uaav和UAAV和AUV之间的水下协调。在协调路径规划模型中,车辆的运动,不同媒体中的约束以及每个相中的优化指数都配制成数学形式。基于粒子群优化(PSO)算法,配积点用于确定控制变量的位置。这些点可以减少计算负载并提高解决方案质量,并且它们根据每个阶段的约束形式分配高度和时刻。此外,生成了解决不可行解决方案的策略,以保证基于PSO的算法的正常运行。仿真结果表明,所提出的两相协调路径规划方法可以产生协调路径,所获得的结果非常接近理论上的最佳解决方案。与整个方法相比,两相方法可以更好地处理每个阶段的复杂约束。

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