The goal of this project is to advance the state of the art in autonomous Cyber-Physical Systems (CPS) by integrating tools from computer science and control theory. With the rise in deployment of autonomous CPS--from automotive to aerospace to robotic systems--there is a pressing need to verify and validate properties of these systems and thereby ensure their safe operation. The work will help establish the scientific basis for test and evaluation methods applicable to CPS, especially as they interact with other agents and the world in highly dynamic ways. This has the potential to inform the development and deployment of complex CPS in a variety of application domains: from (semi-) autonomous cars, to safety features in aviation, to robotic systems for industrial applications and space exploration. The appeal of dynamic CPS will be utilized to broaden participation in computing and engineering.

The vision of this project is to establish the scientific foundations for the verification and validation of highly dynamic safety-critical CPS operating in complex environments. The key novelty is a rigorous approach that leverages control barrier functions on the underlying nonlinear dynamics to provide guarantees of set invariance yielding: safety-critical abstractions on which to specify and verify desired properties, formal methods certifying system-level designs against those properties, and design rules that allow adaptation and machine learning to be integrated with control barrier functions thereby preserving system safety and performance specifications. Proof-of-concept experimental demonstrations will be performed on CPS that are autonomous, dynamic and safety-critical, e.g., robotic systems.

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.