This project brings together theories of brain functions and principles of robotic swarm control to develop smarter swarms and to better understand the neural processes underlying spatial representations, navigation, and planning. Our world is constantly changing, and mammals have evolved the cognitive ability to plan new paths or new strategies as needed. By contrast, autonomous robots are less robust, and often have difficulty operating in complex, changing environments. This research project is grounded in the idea that individual robots in a group can be thought of analogously to neurons in an animal's brain, which interact with one another to form dynamic patterns that collectively signal locations in space and time relative to brain rhythms. This distribution of information across space and time will enable a new paradigm of swarm control, in which swarms automatically adapt to changes in the world in the same way that a rat knows which detour to take around an unexpected obstacle. Unmanned robots are rapidly becoming a crucial technology for commercial, military, and scientific endeavors throughout the nation and across the globe. Critical future applications such as disaster relief and search & rescue will require intelligent spatial coordination among many robots spread over large geographical areas. This project will advance neural swarming as a control paradigm for this next generation of technological development. Additionally, this project will drive an extensive science, technology, engineering, and mathematics education program to bring the concepts of spatial intelligence, hippocampal information processing, and swarm control to high school students to improve literacy in neuroscience and robotics.
The project's goal is to build a unified framework for self-organized, bottom-up control of spatial task planning that synergistically advances theoretical neuroscience and swarm control paradigms. In the project's brain-to-swarm metaphor, neurons are autonomous agents, spikes are agent-based phase signals, and emergent circuit activity is emergent swarm behavior. The approach targets neural computations in hippocampal circuits and related systems that may contribute to online dynamic replanning. The research thrusts comprise data-driven dynamical network and point-process models of neural activity sequences, mathematical analysis of swarming dynamics using matrix manifolds, and autonomous systems simulations in realistic virtual environments. The project will advance understanding of emergent hippocampal dynamics and autonomous methods for dynamic replanning, motivating new research in distributed control. The project's framework may enable mass-scalability for large, agile swarms of simple robotic agents.
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.
