Collaborative Research: FW-HTF: Integrating Cognitive Science and Intelligent Systems to Enhance Geoscience Practice

合作研究：FW-HTF：整合认知科学和智能系统以增强地球科学实践

• 批准号: 1839730
• 负责人: Basil Tikoff
• 金额: $ 50万
• 依托单位: University of Wisconsin-Madison
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-10-01 至 2022-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1839730&HistoricalAwards=false
• 关键词: Collaborative; Research; FW; HTF; Integrating

The Future of Work at the Human-Technology Frontier (FW-HTF) is one of 10 new Big Ideas for Future Investment announced by NSF. The FW-HTF cross-directorate program aims to respond to the challenges and opportunities of the changing landscape of jobs and work by supporting convergent research. This award fulfills part of that aim. This project will make a significant contribution toward the support of future workers in geology. Understanding how geologists reason, plan to collect new data, consider three-dimensional spatial relations, and evaluate uncertainty are critically important for supporting scientists working on applied problems, such as natural resource exploration. This project will enhance existing efforts in geology to collect data using robot drones. Drones allow access to important areas of the world too dangerous to access in person and not visible from satellite or plane. The project will use machine learning to incorporate expert knowledge into drone flights to support effective autonomous data collection. The data will yield improved geological understanding of an important fault system. Findings from the project will improve understanding of uncertainty in volumes and thus improve our understanding of earthquakes and the analyses of petroleum workers. Understanding how expert geologists reason will support new exploration and mapping strategies for human-robot teams working in natural environments. The collaborative efforts of the interdisciplinary team will advance the fields of cognitive science, geology, and machine learning. The integration of cognitive science, robotics, and geology will develop new approaches to field work with human-autonomous systems teams that are faster and more effective than any either human or autonomous system would be acting alone. The project will characterize expert spatial reasoning about 3D relations and uncertainty as geologists collect data to develop a 3D understanding of a new field area, make predictions about future observations, and construct geological models. Errors in reasoning about 3D structures will be used to develop quantitative models of expert uncertainty. These models will be used to help explicitly visualize uncertainty for the experts and to construct cost functions for the robot navigation. The cost functions will include metrics that capture scientific value. The project will develop new approaches to drone exploration and mapping, including machine learning of features of interest to geologists. Drones will autonomously explore and map natural rock formations in canyon environments to support and speed up the data collection and interpretation efforts of field geologists. The project will study the structural geology of the Mecca Hills area of California, a well exposed portion of the San Andreas fault system. Robot drones will collect data about surface features to develop maps of subsurface structures. The cognitive science-infused robot design will employ successful expert strategies and focus on areas where experts are likely to make errors to prioritize exploration of those areas in navigation plans. The proposed strategies will enable 3D surface reconstruction of canyon surfaces. They will also enable better understanding of how to enhance planning and on-the-fly decision making of experts for collecting scientifically important data. The project's foundational work aims to develop an interdisciplinary understanding of how geologists build a scientific understanding of a region over time. It also aims to design autonomous exploration strategies for human-robot teams, and test new ways to support the sequential decisions about where to collect data to maximize scientific impact.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.

人类技术领域（FW-HTF）的工作未来是NSF宣布的未来投资的10个新想法之一。 FW-HTF跨指导计划旨在通过支持融合研究来应对不断变化的工作和工作环境的挑战和机遇。该奖项实现了该目标的一部分。该项目将为未来的地质工人的支持做出重大贡献。了解地质学家如何推理，计划收集新数据，考虑三维空间关系并评估不确定性对于支持从事应用问题的科学家（例如自然资源探索）至关重要。该项目将加强使用机器人无人机收集数据的现有努力。 无人机允许进入世界重要区域太危险，无法亲自进入，而从卫星或飞机上看不到。 该项目将使用机器学习将专家知识纳入无人机航班以支持有效的自动数据收集。 数据将产生对重要断层系统的地质理解。该项目的发现将提高人们对体积不确定性的理解，从而提高我们对地震和石油工人的分析的理解。 了解专家地质学家的推理将如何支持在自然环境中工作的人类机器人团队的新探索和映射策略。跨学科团队的协作努力将推进认知科学，地质和机器学习的领域。认知科学，机器人技术和地质学的整合将开发新的方法来与人类自治系统团队合作，这些团队比任何人类或自治系统都可以单独采取任何行动，而人自主系统团队更快，更有效。该项目将表征有关3D关系和不确定性的专家空间推理，因为地质学家收集数据以对新的现场区域发展3D理解，对未来观察的预测并构建地质模型。 大约3D结构的推理错误将用于开发专家不确定性的定量模型。这些模型将用于帮助明确可视化专家的不确定性，并为机器人导航构建成本功能。成本功能将包括捕获科学价值的指标。该项目将开发用于无人机勘探和映射的新方法，包括对地质学家感兴趣的特征的机器学习。无人机将在峡谷环境中自主探索和绘制自然岩层，以支持和加快现场地质学家的数据收集和解释工作。 该项目将研究加利福尼亚州麦加山地区的结构地质，这是圣安德烈亚斯断层系统的曝光部分。 机器人无人机将收集有关表面特征的数据，以开发地下结构的地图。认知科学的机器人设计将采用成功的专家策略，并专注于专家可能会犯错误以优先考虑导航计划中这些领域的领域。拟议的策略将使峡谷表面的3D表面重建。他们还将更好地理解如何加强专家收集科学重要数据的专家的计划和即时决策。 该项目的基础工作旨在对地质学家如何建立对一个地区的科学理解的跨学科理解。它还旨在为人类机器人团队设计自主探索策略，并测试新的方法，以支持有关在哪里收集数据以最大程度地发挥科学影响的依次决策。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的审查标准来通过评估来通过评估来支持的。

项目成果

Scaffolding geology content and spatial skills with playdough modeling in the field and classroom

在现场和课堂上通过橡皮泥建模搭建地质学内容和空间技能

• DOI: 10.1080/10899995.2022.2071082
• 发表时间: 2022
• 期刊: Journal of Geoscience Education
• 作者: Bateman, Kathryn M.;Ham, Joy;Barshi, Naomi;Tikoff, Basil;Shipley, Thomas F.
• 通讯作者: Shipley, Thomas F.

Strategies for effective unmanned aerial vehicle use in geological field studies based on cognitive science principles

基于认知科学原理的地质野外研究中有效使用无人机的策略

• DOI: 10.1130/ges02440.1
• 发表时间: 2022
• 期刊: Geosphere
• 影响因子: 2.5
• 作者: Bateman, Kathryn M.;Williams, Randolph T.;Shipley, Thomas F.;Tikoff, Basil;Pavlis, Terry;Wilson, Cristina G.;Cooke, Michele L.;Fagereng, Ake
• 通讯作者: Fagereng, Ake

Hit-and-run model for Cretaceous–Paleogene tectonism along the western margin of Laurentia

劳伦大陆西缘白垩纪—古近纪构造运动的肇事逃逸模型

• 发表时间: 2023
• 期刊: Memoir Geological Society of America
• 作者: Tikoff, B.;Housen, B.A.;Maxson, J.A.;Nelson, E.M.;Trevino, S.;and Shipley, T.F.
• 通讯作者: and Shipley, T.F.