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

概述：沿海海洋是一个复杂的环境，由大气、海洋学、河口/河流和陆海过程的相互作用驱动，这些过程导致了动态的沿海特征，如水华、缺氧带和羽流（河口、石油、污染物）。有效地观察和量化这些特征需要同时，快速测量不同的水性质，以捕捉其变异性。本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，并有助于海岸勘探，以确保机器人在“正确的地点和时间”出现，以提供最有效的测量。技术说明：由于没有一个单一的模型来吸收所有可用的物理和生物地球化学数据，以提供可靠的海洋特征视图，因此本项目采用了人类专业知识、模型改进和分析自适应采样相结合的方法。在决策支持系统中，人类决策与概率建模和学习相结合，使环境领域模型发现和改进成为可能。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，以及创建多分辨率、多变量采样方法，扩展了多机器人自适应采样的最新技术。这些进步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，基于采样的模型优化，涉及小尺度、高分辨率的机器人采样轨迹的自主合作选择和执行。这两项挑战都涉及处理多变量、多分辨率、随时间变化的场。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估研究。更广泛的影响：以非计算机专家可解释的形式整合各种数据的决策支持将对一系列领域产生更广泛的影响，包括海洋和空间探索，环境灾害响应以及军事和国土安全。海洋科学界将有一个新的强大的工具来增加他们对动态沿海现象的理解，政策制定者将有一个重要的工具来帮助制定影响沿海社区的决策。预计所开发的方法将广泛适用于目标驱动的勘探和大面积表征的一般任务。该项目将涉及研究生，他们将在跨学科的背景下接受培训。项目结果将在同行评议的科学文献中发布，并通过项目网站http://robotics.usc.edu/comeco.html发布

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