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

CDI-Type I：协作研究：协作多机器人探索沿海海洋概述：沿海海洋是一个复杂的环境，由大气、海洋、河口/河流和陆地-海洋过程相互作用驱动，这些过程导致动态的海岸特征，如水华、缺氧区和羽流(河口、石油、污染物)。对这些特征的有效观察和量化需要同时、快速地测量不同的水性质，以捕捉其可变性。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，并帮助沿海勘探，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。技术说明：由于没有一个单一的模型同化所有可用的物理和生物地球化学数据来提供可靠的海洋特征视图，因此有利于将人类专业知识、模型改进和本项目采用的分析性自适应采样相结合。人类决策与决策支持系统中的概率建模和学习相结合，从而能够发现和改进环境领域模型。该项目通过研究环境场结构与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技术水平。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，它涉及将大规模、低分辨率的数据与人类的科学专业知识相结合，以做出及时的、基于模型的资产配置决策。第二种是基于采样的模型求精，包括小规模、高分辨率的自主协作选择和执行机器人采样轨迹。这两个挑战都涉及处理多变量、多分辨率、时间演变的领域。该项目包括使用水下机器人进行沿海海洋勘探的可行性和评价研究。广泛影响：以非计算机专家可以解释的形式整合各种数据的决策支持将在海洋和空间探索、环境灾害应对以及军事和国土安全等一系列领域产生更广泛的影响。海洋科学界将有一个新的强有力的工具来增进他们对动态沿海现象的了解，而政策制定者将成为帮助影响沿海社区的决策的重要工具。预期所开发的方法将广泛适用于以目标为导向的大片区域勘探和表征的一般任务。该项目将涉及研究生，他们将在跨学科的背景下接受培训。项目成果将在同行评议的科学文献中以及通过项目网站传播，网址为：http://robotics.usc.edu/comeco.html

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