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首页> 外文期刊>Ocean Engineering >Dynamic collision avoidance scheme for unmanned surface vehicles under complex shallow sea Environments
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Dynamic collision avoidance scheme for unmanned surface vehicles under complex shallow sea Environments

机译:复杂浅水环境下无人面车辆的动态碰撞避免方案

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摘要

In this paper, collision avoidance (CA) scheme for unmanned surface vehicles (USVs) under complex shallow sea environments which contain static sea surface (Static), undersea obstacles (UO) and surface island with reefs (IR) is proposed. The main challenge of this paper is that CA objects not only include surface obstacles, UO and IR, but more importantly, they will be affected by dynamic water level. It is difficult, if not impossible, to employ conventional CA schemes for USVs. The salient features of the proposed scheme are: 1) A UO model which incorporates time-varying water level is developed. 2) The smoothing edge (SE) method of island of IR that is based on fuzzy Gauss mixture model is enhanced by introducing fuzzy membership to Gaussian mixture model. 3) A real-time collision probability (RTCP) algorithm under IR with time-varying water level which obeys the Poisson and Gauss distribution is developed. By combining the UO-FMM and the SE-RTCP, the proposed CA scheme is able to handle uncertainties in complex shallow sea environments. Comparative studies with the state of the art demonstrate that the proposed CA scheme is superior in terms of safety and yaw cost consumption.
机译:在本文中,提出了含有静态海面(静电),海底障碍物(UO)和带珊瑚礁(IR)的复杂浅海环境下的无人面车辆(USV)的碰撞避免(USV)。本文的主要挑战是CA对象不仅包括地表障碍物,UO和IR,而且更​​重要的是,它们将受到动态水位的影响。如果不是不可能的话,难以使用传统的USV方案。所提出的方案的突出特征是:1)开发了一种包含时变水位的UO模型。 2)基于模糊高斯混合模型的IR岛的平滑边缘(SE)方法通过向高斯混合模型引入模糊的成员来增强。 3)利用时变水位下的IR实时碰撞概率(RTCP)算法,开发了宠物泊松和高斯分布的时变水位。通过组合UO-FMM和SE-RTCP,所提出的CA方案能够处理复杂的浅海环境中的不确定性。与本领域的比较研究表明,所提出的CA方案在安全性和横摆成本消耗方面优越。

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