Autonomous Vehicles, Robotics and Control Research

Autonomous vehicles and robots, such as unmanned aircraft, un-crewed ships, self-driving cars, and autonomous planetary rovers, are becoming reality and promise great future growth in technology maturation and economic return.

All such autonomous agents have one thing in common: feedback control/decision loops at different levels of the command hierarchy and on different reaction time-scales. Each feedback loop entails: a command/goal generator, which could be either a human operator or computational intelligence that resides in a higher level of the decision-making hierarchy; a sensor set that assesses the situation and the state of the agent; a decision maker or controller that proposes an action using computational algorithms based on the discrepancy between the commanded and the sensed situation/state, as well as the capability and characteristics of the agent; and a vehicle actuator that will carry out the action.

The first three items are also known as Guidance, Navigation, and Control (GNC), with the last item being a feedback loop with the control decision as its guidance command. The Control Systems Group at the Russ College studies and develops the GNC algorithms using theories of dynamics, hybrid systems, machine cognition, artificial intelligence, and neural networks, with applications in automatic and autonomous vehicles and robots.