S&AS: INT: COLLAB: Goal-driven Marine Autonomy with Application to Fisheries Science and Management

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• 批准号: 1849137
• 负责人: Catherine Edwards
• 金额: $ 25万
• 依托单位: University of Georgia Research Foundation Inc
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-02-15 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1849137&HistoricalAwards=false
• 关键词: S& INT; COLLAB; Goal; driven

Marine robots can be used to accurately map and track marine life, leading to a better interpretation of variability and migration patterns that are important to fisheries managers in marine protected areas (MPAs). Principles of engineering and oceanography can be used to maximize the impact of a network of marine robots, but because fisheries managers, oceanographers, and roboticists have different perspectives and knowledge bases, it can be difficult to take advantage of cutting-edge research in each field without significant effort to translate among the groups. Researchers will develop a computational interface that translates human-specified missions of fisheries managers into multi-level planning for a fleet of marine robots to monitor fish populations in a dynamic coastal ocean environment. The system will be designed with input from fisheries managers through a series of workshops, and will be field-tested at Gray's Reef National Marine Sanctuary, a federally-managed MPA off the coast of Georgia. The research will lead to more accurate and effective ways to monitor fish populations in MPAs, as well as breakthroughs in key areas of artificial intelligence and autonomous systems. Many of the results will be applicable to other smart and autonomous systems in challenging environments. In addition, the project will train graduate students and broaden undergraduate education in Science, Technology, Engineering, and Mathematics (STEM), and offer a number of outreach activities, including working with the University of Georgia Marine Extension service to develop a summer camp for middle and high school students.The project is focused on developing an intelligent physical system (IPS) that consists of a heterogeneous fleet of marine robots, cooperatively tracking fish movement and surveying the habitat with minimum request for human intervention. The IPS will translate the human-specified missions of fisheries managers into goal-driven task designs for each robot, and automatically generate executable plans for the networked mobile sensing agents. The system will autonomously and persistently collect in-situ measurements and acoustic detections of fish species while maintaining multi-scale data streams and constructing multiple spatial-temporal maps reflecting the conditions of the ecosystem. The research aims to discover the hotspots (e.g., spatial locations with sustained congregations of fish), as well as illuminate more information about how and when fish move among these hotspots. This goal is quite challenging due to a number of gaps between project needs and the state-of-art autonomy research. Researchers will address the challenges through new developments that accomplish three main tasks: (1) Developing the goal-driven marine autonomy for fish habitat survey, (2) realizing the goal-driven autonomy on physical systems, and (3) evaluating the developed framework through real-life field work, experiments, and data analysis.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

海洋机器人可用于准确地绘制和跟踪海洋生物，从而更好地解释对海洋保护区（MPA）渔业经理至关重要的可变性和迁移模式。 工程和海洋学的原则可用于最大程度地提高海洋机器人网络的影响，但是由于渔业经理，海洋学家和机器人主义者具有不同的观点和知识基础，因此在没有大量努力的情况下，很难利用每个领域的尖端研究来在小组之间进行大量翻译。研究人员将开发一个计算界面，将人类指定的渔业经理任务转化为多层计划，以计划在动态的沿海海洋环境中监测鱼类种群的船队。该系统将通过一系列研讨会的渔业经理的意见设计，并将在乔治亚州海岸的联邦政府管理的MPA的Gray's Reef National Marine Sanctuary进行现场测试。 这项研究将导致更准确，更有效的方法来监测MPA中的鱼类种群，以及人工智能和自主系统的关键领域的突破。许多结果将适用于具有挑战性的环境中的其他智能和自主系统。 此外，该项目将培训研究生，并扩大科学，技术，工程和数学（STEM）的大学教育，并提供许多外展活动，包括与佐治亚大学海洋扩展服务服务，以开发中学和高中生的夏令营。最低要求人为干预。 IPS将将渔业经理的人类指定任务转换为每个机器人的目标驱动任务设计，并自动为网络移动传感代理生成可执行计划。该系统将自主和持续收集鱼类物种的原位测量和声学检测，同时维持多尺度数据流并构建反映生态系统条件的多个时空图。该研究旨在发现热点（例如，持续的鱼会众的空间位置），并阐明有关鱼类如何以及何时在这些热点中移动的更多信息。由于项目需求与最先进的自治研究之间的许多差距，该目标是非常具有挑战性的。研究人员将通过完成三个主要任务的新发展应对挑战：（1）开发目标驱动的鱼类栖息地调查的海洋自主权，（2）实现目标驱动的物理系统自治，（（3）通过现实生活中的实验和数据分析来评估开发的框架。更广泛的影响审查标准。

项目成果

Modeling Acoustic Telemetry Detection Ranges in a Shallow Coastal Environment

浅海环境中的声学遥测探测范围建模

• DOI: 10.1145/3491315.3491331
• 发表时间: 2021
• 期刊: Proc WUWNet'21
• 作者: McQuarrie, Frank;Brock Woodson, Clifton;R. Edwards, Catherine
• 通讯作者: R. Edwards, Catherine

Learning and detecting abnormal speed of marine robots

• DOI: 10.1177/1729881421999268
• 发表时间: 2021-03-01
• 期刊: INTERNATIONAL JOURNAL OF ADVANCED ROBOTIC SYSTEMS
• 影响因子: 2.3
• 作者: Cho, Sungjin;Zhang, Fumin;Edwards, Catherine R.
• 通讯作者: Edwards, Catherine R.

Uncrewed Ocean Gliders and Saildrones Support Hurricane Forecasting and Research

无人驾驶海洋滑翔机和无人机支持飓风预报和研究

• DOI: 10.5670/oceanog.2021.supplement.02-28
• 发表时间: 2022
• 期刊: Oceanography
• 影响因子: 2.8
• 作者: Miles, T.