Control, Robotics, Autonomy, and Learning (CRAL)

Control, Robotics, Autonomy, and Learning (CRAL)  

This broad area is addressing in a balanced manner both theoretical foundations and significant high impact applications in dynamical systems, multi-agent decision-making, intelligent control, dynamic planning, feedback, adaptation, robustness, and learning. Principles and algorithms of optimization, dynamic games, formal methods, integration of logic and optimization, temporal logic, networked control systems, cyber-physical Cisystems, hybrid dynamical systems, interrelationships between dynamical systems and control with AI and ML are investigated. Applications include autonomous vehicles, robotic manipulators, human-machine collaboration, trustworthy autonomy, collaborating UGVs and UAVs, control of swarms, security and privacy in control, learning enabled control, smart grids, sensor and communication networks, social networks. Experimental facilities supporting the research include laboratories in the Maryland Robotics Center, the Maryland Hybrid Networks Center (both parts of the Institute for Systems Research), and the E. A. Fernandez Idea Factory. Synergistic efforts with allied areas of communications, computing, information theory, signal processing, and security offer many opportunities for cutting-edge cross-disciplinary research and industrial collaboration.

Faculty in this area of research include:

• Eyad Abed
• Tom Antonsen
• John Baras
• Calin Belta
• P.S. Krishnaprasad
• Nuno Martins
• André Tits
• Kaiqing Zhang