Automation & Control
Automation & Control combines engineering with machine-learning in order to provide industrial systems with the information necessary to work in an automatic and controlled manner. Research areas include: atomic force microscopy, bio-inspired control, discrete-event systems, formal languages for robot mission specification, hybrid systems, image-guided surgery, networked control systems, robot path planning and control, robotic swarms, and UAV flight control.
Achieving Consensus Among Autonomous Dynamic Agents using Control Laws that Maintain Performance as Network Size Increases
Recent advances in automation and robotics have created a pressing need for new “protocols,” that is, for algorithms or control laws that allow teams of multiple autonomous agents to cooperate and accomplish complex tasks. Unfortunately, many of the best protocols for multi-agent coordination problems suffer from scalability issues, that is, while they perform well when […]
Compressive Robotic Systems: Gaining Efficiency Through Sparsity in Dynamic Environments
This project investigates autonomous control and coordination of a group of robots that are tasked to explore, map, or monitor the environment they are in. The project aims to enhance the capabilities of such a group of robots by integrating Compressive Sensing for data compression. Compressive sensing enables robots to quickly extract information from their […]
CPS: Synergy: Collaborative Research: A Cyber-Physical Infrastructure for the “Smart City”
The project aims at making cities “smarter” by engineering processes such as traffic control, efficient parking services, and new urban activities such as recharging electric vehicles. To that end, the research will study the components needed to establish a Cyber-Physical Infrastructure for urban environments and address fundamental problems that involve data collection, resource allocation, real-time […]
CPS: Synergy: Data Driven Intelligent Controlled Sensing for Cyber Physical Systems
The goal of this project is to develop the foundations of a control and optimization science base for sensor networks viewed as complex systems operating in an uncertain and potentially adverse environment. The approach taken is a combination of addressing fundamental research issues while maintaining a focus on a specific target application domain, a manufacturing […]