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

University Of Wisconsin-Madison, Madison WI

Investigators

Abstract

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.