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Full Stream Name: Autonomous Intelligent Robotics

Credit Options: Spring: CS 378  (TTh 3:30-5PM), Fall: CS378 (TTh 3:30-5PM)

Can intelligent robots effectively coordinate to aid humans? 

The goal of this stream is to create a system of fully autonomous robots inside the new Gates complex to aid people inside the building. Students will learn about and contribute to cutting-edge research in artificial intelligence and robotics.

Students in the Autonomous Intelligent Robotics stream are designing software for a system of robots that will exist within the new Bill and Melinda Gates Computer Science Complex.  The stream's goal is to enable robots, and associated software agents, to interact with building visitors and residents.

The stream is a successor to the stream called Autonomous Vehicles Driving in Traffic, which was motivated by the DARPA Urban Challenge. In Fall 2007, the US Defense Advanced Research Projects Agency (DARPA) held the Urban Challenge, a street race between fully autonomous vehicles. Unlike previous challenges, the Urban Challenge vehicles had to follow the California laws for driving, including properly handling traffic. UT undergraduate students in Spring 2007 wrote the software to pass the first two levels of tests for the Urban Challenge. As a result, the Austin Robot Technology/UT vehicle participated in the National Qualifying Event. After the qualification tests, only 11 of the original 83 teams were chosen to be in the final race. The UT student programmed vehicle made the top 21 teams, but not the top 11.

Now, FRI students are working on a different autonomous robotics challenge, namely the multi-robot interactive system described above. Topics include indoor navigation, wifi localization, human interaction, activity recognition, multi-robot coordination, and many others.

This is a large programming project in which students experience many CS ideas that they can learn in detail as juniors or seniors in the Department of Computer Science. These include: software engineering, threads, message passing, distributed computing, real-time systems, and AI algorithms. Additionally, students with engineering backgrounds can apply control theory and other engineering principles to the design and control of the robots. Students also learn about, and often contribute to, cutting-edge research in robotics and autonomous agents.

 

Contact information:

Faculty Leader: Dr. Peter Stone

Research Educator: Dr. Jivko Sinapov

Fall 2016 Course website: http://www.cs.utexas.edu/~jsinapov/teaching/cs378/

To read more about Dr. Stone's research, visit his website.

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Computer Science