Society is steadily moving toward a world in which autonomous vehicles roam the streets side-by-side with human-driven vehicles and unmanned aerial vehicles are integrated into the national airspace. These autonomous systems will be tasked with missions such as search, rescue, surveillance, reconnaissance, mapping, farming, fire fighting, and transportation. Future autonomous systems will operate in unfamiliar areas with minimal or no human interaction for prolonged periods of time. The luxury of building prior detailed maps of these environments could be (1) prohibitive (e.g., disaster areas), (2) impractical (e.g., signal landscapes and congested downtowns), or (3) economically not viable (e.g., hospital buildings and national forests). With no human-in-the-loop before or during operation, one expects future autonomous systems to (1) possess full situational awareness and (2) gather sufficient information about their environment. These two tasks need to seamlessly integrate into the overall mission of the autonomous system. Current autonomous systems are far from possessing these capabilities, and the current analytical tools are insufficient to deal with this emerging class of problems.

This project will develop a coherent analytical foundation and a suite of algorithms and tools for autonomous systems deployed in unknown, dynamic stochastic environments to optimally gather sufficient information to successfully accomplish their mission. The research specifically considers autonomous systems with limited sensing, computation, actuation, and communication capabilities. This research will study a new class of information optimization measures, which possess desirable convexity properties (enabling real-time execution) and separability properties (enabling near-lossless distributed implementation among agents). This research aims to establish fundamental relationships between performance and computational complexity in the presence of varying degrees of environmental uncertainty. These relationships will enable principled navigation of these complex trade-offs, leading to autonomous identification and adoption of the optimal information gathering strategy.

This project has a vertically-integrated education plan spanning K-12, undergraduate, and graduate students. The project will also train in-service and pre-service K-12 teachers to apply Next Generation Science Standards (NGSS) - a set of science standards that integrate rigorous content and application, reflecting how STEM is practiced in the real world. This research has far-reaching impact - it will evolve autonomous systems from sensing the environment to making sense of the environment, bringing new capabilities in environments where direct human control is physically or economically not possible.

National Science Foundation (NSF)
Division of Computer and Network Systems (CNS)
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Marilyn McClure
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University of California Irvine
United States
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