CDI-Type I: Collaborative Research: Collaborative Multi-robot Exploration of the Coastal Ocean

CDI-I型：协作研究：沿海海洋的协作多机器人探索

• 批准号: 1125015
• 负责人: Gaurav Sukhatme
• 金额: $ 34.01万
• 依托单位: University of Southern California
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-01 至 2015-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1125015&HistoricalAwards=false
• 关键词: CDI; Type; Collaborative; Research; Multi

CDI-Type I: Collaborative Research: Collaborative Multi-robotExploration of the Coastal Ocean (COMECO)Overview: The coastal ocean is a complex environment driven by the interaction of atmospheric, oceanographic, estuarine/riverine, and land-sea processes, which result in dynamic coastal features such as blooms, anoxic zones, and plumes (estuarine, oil, pollutant). Effective observation and quantification of these features require simultaneous, rapid measurement of diverse water properties to capture its variability. This project aims to synthesize and understand the basic principles of environmental sensing based on the integration of adaptive robotic sampling with human decision-making. The techniques being developed augment existing ocean models and aid coastal exploration to ensure that robots are present at the "right place and time" to provide the most effective measurements.Technical Description: The absence of a single model assimilating all available physical and biogeochemical data to provide a reliable view of ocean features favors the combination of human expertise, model refinement, and analytical adaptive sampling adopted in this project. Human decision-making is coupled with probabilistic modeling and learning in a decision support system enabling environmental field model discovery and refinement. The project extends the state of the art in multirobot adaptive sampling by investigating the relationship between environmental field structure and sampling performance, developing improved field boundary tracking techniques, and creating methods for multi-resolution, multivariable sampling. These advances are being made by addressing two broad research challenges. The first, Model-Based Asset Allocation, involves synthesis of large-scale, low-resolution data with human scientific expertise to make timely, model-informed asset allocation decisions. The second, Sampling-Based Model Refinement, involves small-scale, high-resolution autonomous cooperative selection and execution of robot sampling trajectories. Both challenges involve the handling of multivariate, multi-resolution, temporally evolving fields. The project includes a feasibility and evaluation study in coastal ocean exploration using underwater robots.Broader Impacts: Decision support with diverse data integrated in a form that is interpretable by a non-computer specialist will have a broader impact applicable to a range of domains, including ocean and space exploration, environmental disaster response and military andhomeland security. The ocean science community will have a new and powerful tool to augment their understanding of dynamic coastal phenomena and policy makers an important tool to aid decision making impacting coastal communities. It is expected that the methods developed will be broadly applicable to the general task of goal-driven exploration and characterization of large areas. The project will involve graduate students who will be trained in an interdisciplinary context. The project results will be disseminated in the peer-reviewed scientific literature as well as via the project website at: http://robotics.usc.edu/comeco.html

CDI-I型：合作研究：沿海海洋的协作多机器人勘探（COMECO）概述：沿海海洋是一个复杂的环境，由大气，海洋，河口/河流和陆海过程的相互作用驱动，导致动态沿海特征，如水华，缺氧区和羽流（河口，石油，污染物）。要有效地观察和量化这些特征，就需要同时、快速地测量各种水的性质，以捕捉其变化性。该项目旨在综合和理解基于自适应机器人采样与人类决策相结合的环境传感的基本原理。正在开发的技术增强了现有的海洋模型，并有助于沿海勘探，以确保机器人出现在“正确的地点和时间”，以提供最有效的测量。缺乏一个单一的模型来吸收所有可用的物理和地球化学数据，以提供对海洋特征的可靠看法，这有利于人类专业知识，模型改进，和分析型自适应抽样。人类的决策与概率建模和学习相结合，在决策支持系统中，使环境场模型的发现和细化。该项目通过调查环境场结构和采样性能之间的关系，开发改进的场边界跟踪技术，并创建多分辨率，多变量采样方法，扩展了多机器人自适应采样的最新技术。这些进展是通过解决两个广泛的研究挑战而取得的。第一种是基于模型的资产配置，涉及将大规模、低分辨率数据与人类科学专业知识相结合，以及时做出基于模型的资产配置决策。第二，基于采样的模型精化，涉及小规模，高分辨率的自主合作选择和执行机器人采样轨迹。这两个挑战涉及多变量，多分辨率，时间演变领域的处理。更广泛的影响：以非计算机专家可以解释的形式综合各种数据的决策支持将产生更广泛的影响，适用于一系列领域，包括海洋和空间探索、环境灾害反应以及军事和国土安全。海洋科学界将有一个新的和强大的工具，以增加他们的动态沿海现象和决策者的理解，一个重要的工具，以帮助决策影响沿海社区。预计所制定的方法将广泛适用于以目标为导向的勘探和确定大区域特征的一般任务。该项目将涉及研究生，他们将在跨学科背景下接受培训。项目结果将在同行评审的科学文献中以及通过项目网站http://robotics.usc.edu/comeco.html传播。