The Daniel Guggenheim School of Aerospace Engineering supports wide-ranging research on robotic autonomous systems -- intelligent machines with the ability to perceive, reason, and act on their environment in order to accomplish a specific task.
Autonomous systems are able to make "high-level” decisions with limited information and in the presence of uncertainty. They often incorporate an element of learning which allows them to handle unpredictable scenarios.
This contrasts with more classical control systems, which tend to operate in more well-defined, structured environments. Classic control systems typically execute on “low-level” commands such as tracking and set-point regulation.
The study of autonomous robotic systems holds tremendous potential for high-impact exploration, as these systems often share the environments with humans and therefore need to be cognizant of this fact, be able to interpret user intend, and adjust their behavior accordingly.