The Robotarium is a remotely accessible multi-robot platform. And, safety is of central importance to the successful realization of any remote-access test-bed and failure to enforce safety could result in injury in local operators and damaged equipment.

Our  proposal’s main objective is to realize cyber-physical platform and principles for (i) interrogating global modalities of intracellular transport with causative factors isolated at the single-molecule scale and (ii) realizing efficient and robust infrastructure for transporting micron/molecular scale cargo using distributed strategies We are realizing  in-vitro, a transport network with roadways formed by microtubules where motorproteins, kinesin and dynein, will form vehicles ferrying cargo.

The electric power grid is a complex cyber-physical system (CPS) that forms the lifeline of modern society. Cybersecurity and resiliency of the power grid is of paramount importance to national security and economic well-being. CPS security testbeds are enabling technologies that provide realistic experimental platforms for the evaluation and validation of security technologies within controlled environments, and they also enable the exploration of robust security solutions.

The objective of this project is to improve the performance of autonomous systems in dynamic environments by integrating perception, planning paradigms, learning, and databases. For the next generation of autonomous systems to be truly effective in terms of tangible performance improvements (e.g., long-term operations, complex and rapidly changing environments), a new level of intelligence must be attained.

This project addresses how cyber physical walking systems (CPWS) can be designed to be safe, secure, and resilient despite a variety of unanticipated disturbances and how real-time dynamic control and behavior adaptation can be achieved in a diversity of environments.

The goal of this project is to demonstrate that new cyber-physical architectures will enable closed-loop sensor networks to be shared among multiple applications and to dynamically allocate sensing and computing resources necessary to analyze sensor data and perform sensor actuation. The sharing of sensor network infrastructures will make the provision of data (e.g., weather information) more cost efficient and will create cyber infrastructures, which will result in a dramatic increase in the number of sensor networks available for use.

A significant challenge in dimensional accuracy control of cyber-physical additive manufacturing systems (CPAMS) is the specification of geometric shape deviation models. The current practice of constructing tailor-made deviation models for each combination of computer- aided design model, additive manufacturing (AM) process, and process setting is impractical and inefficient for general application in CPAMS. We present a new framework and class of  Bayesian neural networks for automated and efficient deviation model building in CPAMS.

The objective of this work is to generate new fundamental science for cyber-physical systems (CPSs) that enables more accurate and faster trajectory synthesis for controllers with nonlinear plants, or nonlinear constraints that encode obstacles. The approach is to utilize hybrid control to switch between models whose accuracy is normalized by their computational burden. This synergistic approach is why we deem our proposed work will enable Computationally Aware Cyber-Physical Systems.