The current lack of toolchain for high confidence testing, validation and verification of advanced, connected and automated/autonomous vehicles can impede and even entirely prevent the introduction of such vehicles into mass production. To address this challenge, this projects develops theory, methods, and tools for generating and optimizing test trajectories and data inputs that can maximize opportunities to uncover faults in both physical and cyber domain in future automotive vehicles.

Episodic brain disorders such as epilepsy have a considerable impact on a patient’s productivity and quality of life and may be life-threatening when seizures cannot be controlled with medications. We will create a second generation brain-implantable sensing and stimulating device (BISSD) based on CPS principles and practice. The BISSD will be composed of modules placed intracranially to continuously monitor brain state and vulnerability to seizure and intervene with electrical stimulation to block the development of seizure.

The broad goal of this project is to advance both the foundations and the enabling technologies in the field of human-machine systems, with a focus on exercise and rehabilitation machines. One of the motivations of our project is the observation that advances in exercise devices are mostly seen in their haptic interfaces rather than their intrinsic capabalities. Also, our collaboration with NASA on exercise machines for astronauts has motivaed us to investigate this aspect of human-machine interaction.

This project aims at deriving a scalable, correct-by-construction formal design methodology for multi-robot systems that can guarantee the accomplishment of high-level team missions through automatic synthesis of local coordination mechanisms and control laws. The basic idea is to decompose the team mission into individual subtasks such that the design can be reduced to local synthesis problems for individual robots. Multidisciplinary approaches combining hybrid systems, supervisory control, regular inference and model checking are utilized to achieve this goal.

The objective of this research is to develop the theory, hardware and computational infrastructure that will enable automatically transforming user-defined, high-level tasks into correct, low-level perception informed control and configurations for modular robots. Modular robots are composed of simple individual modules with limited sensing and actuation; while each module can locomote in the environment, connecting multiple modules in different configurations allows modular robots to perform

We present a bifurcation analysis of electrical alternans in the two-current Mitchell-Schaeffer (MS) cardiac-cell model using the theory of -decidability over the reals. Electrical alternans is a phenomenon characterized by a variation in the successive Action Potential Durations (APDs) generated by a single cardiac cell or tissue.

Optimization algorithms used in a real-time and safety-critical context offer the potential for considerably advancing robotic and autonomous systems by improving their ability to execute complex missions. However, this promise cannot happen without proper attention to the considerably stronger operational constraints that real time, safety-critical applications must meet, unlike their non-real-time, desktop counterparts.