Research

This page summarizes our ongoing research directions. See Publications for technical details!

Current Research Areas

Privacy in control systems

This research thread is developing new theoretical tools to provide privacy to the types of data that are generated and used by decision-making in control systems.

Computationally constrained autonomy

This work is developing new autonomy strategies for computationally constrained systems, with an emphasis on mobile autonomous platforms.

Communication-denied autonomy

This research is developing safe, high-performance decision algorithms for multi-agent systems in which communicating is not possible.

Deceptive decision-making

This work is producing decision algorithms that accomplish control objectives and mislead observers about what those objectives are.

Non-convex optimization

This work is developing new algorithms for non-convex optimization with guaranteed robustness and convergence properties.

Applied category theory

This work is using tools from category theory to develop and characterize new optimization algorithms and control designs.

Acknowledgements

The CORE Lab gratefully acknowledges support from Lockheed Martin, AFOSR, AFRL, NSF, ONR, DARPA, USAFA, and Google.