...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Ocean Dynamics >Autonomous underwater vehicle teams for adaptive ocean sampling: a data-driven approach
【24h】

Autonomous underwater vehicle teams for adaptive ocean sampling: a data-driven approach

机译：自主水下航行器团队进行自适应海洋采样：数据驱动方法

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

The current technological developments in autonomous underwater vehicles (AUVs) and underwater communication have nowadays allowed to push the original idea of autonomous ocean sampling network even further, with the possibility of using each agent of the network not only as an operative component driven by external commands (model-driven) but as a reactive element able to act in response to changing conditions as measured during the exploration (data-driven). With this paper, we propose a novel data-driven algorithm for AUVs team for adaptive sampling of oceanic regions, where each agent shares its knowledge of the environment with its teammates and autonomously takes decision in order to reconstruct the desired oceanic field. In particular, sampling point selection is made in order to minimize the uncertainty in the estimated field while keeping communication contact with the rest of the team and avoiding to repeatedly sampling sub-regions already explored. The proposed approach is based on the use of the emergent behaviour technique and on the use of artificial potential functions (interest functions) to achieve the desired goal at the end of the mission. In this way, there is no explicit minimization of a cost functional at each decision step. The oceanic field is reconstructed by the application of radial basis functions interpolation of irregularly spaced data. A simulative example for the estimation of a salinity field with sea data obtained using the Mediterranean Sea Forecasting System is shown in the paper, in order to investigate the effect of the different uncertainty sources, including sea currents, on the behaviour of the exploration team and ultimately on the reconstruction of the salinity field.
机译：如今，自主水下航行器（AUV）和水下通信的当前技术发展进一步推动了自主海洋采样网络的原始构想，并有可能不仅将网络的每个代理用作外部命令驱动的可操作组件， （模型驱动），但作为一种反应性元素，能够响应勘探过程中所测量的变化条件而起作用（数据驱动）。通过本文，我们为AUV团队提出了一种新的数据驱动算法，用于海洋区域的自适应采样，其中每个代理与队友共享其对环境的了解，并自主做出决策以重建所需的海洋领域。特别是，采样点的选择是为了最大程度地减少估计字段中的不确定性，同时保持与团队其他成员的联系，并避免重复采样已经探索过的子区域。所提出的方法是基于紧急行为技术的使用和人工潜在功能（兴趣功能）的使用，以在任务结束时实现期望的目标。这样，在每个决策步骤都没有明确最小化成本函数。通过使用不规则间隔数据的径向基函数插值来重建海洋场。本文展示了一个模拟示例，该示例使用地中海预测系统获得的海面数据估算盐度场，以研究包括海流在内的各种不确定性源对勘探团队行为的影响。最终在盐分领域的重建。

著录项

  • 来源
    《Ocean Dynamics》 |2011年第11期|p.1981-1994|共14页
  • 作者

    A. Munafo; E. Simetti; A. Turetta; G. Casalino; A. Caiti;

  • 作者单位

    Inter-university Center on Integrated Systems for the Marine Environment, Centro E. Piaggio, University of Pisa, Largo L. Lazzarino, 1,56122 Pisa, Italy;

    Inter-university Center on Integrated Systems for the Marine Environment, Department of Communication,Computer and System Sciences, University of Genoa,Via Opera Pia 13,16145 Genoa, Italy;

    Inter-university Center on Integrated Systems for the Marine Environment, Department of Communication,Computer and System Sciences, University of Genoa,Via Opera Pia 13,16145 Genoa, Italy;

    Inter-university Center on Integrated Systems for the Marine Environment, Department of Communication,Computer and System Sciences, University of Genoa,Via Opera Pia 13,16145 Genoa, Italy;

    Inter-university Center on Integrated Systems for the Marine Environment, Centro E. Piaggio, University of Pisa, Largo L. Lazzarino, 1,56127 Pisa, Italy;

  • 收录信息
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    autonomous underwater vehicles (AUVS); vehicles cooperation; data-driven approach; ocean sampling; autonomous ocean sampling networks;

    机译：自动水下航行器（AUVS）;车辆合作;数据驱动的方法;海洋采样;自主海洋采样网络;

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号