Abstract: This grant provides funding for the formulation of a data model, and trajectory planning platform and methodology to execute a fully digital 3D, 5-axis machining capability. Research will be performed on methods for utilizing multiple Graphical Processor Units (GPUs), which are readily available, parallel digital processing hardware in these calculations.

Abstract: The CrAVES project seeks to lay down intellectual foundations for credible autocoding of embedded systems, by which graphical control system specifications that satisfy given open-loop and closed-loop properties are automatically transformed into source code guaranteed to satisfy the same properties. The goal is that the correctness of these codes can be easily and independently verified by dedicated proof checking systems.

Abstract: The CrAVES project seeks to lay down intellectual foundations for credible autocoding of embedded systems, by which model-level control system specifications that satisfy given open-loop and closed-loop properties are automatically transformed into source code guaranteed to satisfy the same properties. The goal is that the correctness of these codes can be easily and independently verified by dedicated proof checking systems.

Abstract: This project explores balancing performance considerations and power consumption in cyber-physical systems, through algorithms that switch among different modes of operation (e.g., low-power/high-power, on/off, or mobile/static) in response to environmental conditions. The main theoretical contribution is a computational, hybrid optimal control framework that is connected to a number of relevant target applications where physical modeling, control design, and software architectures all constitute important components.

Abstract: We propose a dual expert algorithm (DEA) to assist a mobile robot in learning a person’s preference of moving direction when the human encounters the robot in a typical hallway. When in use in hospital and office environments, a mobile robot will routinely encounter people who are not its primary user. These people need to feel comfortable around the robot for its implementation to be a success.

Abstract: 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.