Jason O'Kane

Assistant Professor
Department of Computer Science and Engineering
University of South Carolina

Office: Swearingen Engineering Center 3A58
315 Main Street
Columbia, SC 29208
+1-803-777-1791 (office)
My email address is here.

Teaching

Fall 2008: CSCE 774 - Robotics Systems
Spring 2008: CSCE 574 - Robotics
Fall 2007: CSCE 350 - Data Structures and Algorithms

Research

My research is in algorithmic robotics.

Publications

I'm recruiting! I am seeking graduate students to work with me on several different research projects. If you are interested in helping make robots more autonomous, more robust, and less expensive, stop by for a chat.

Comparing Robot Systems

Real robots must effectively collect, interpret, and act upon sensor data. How sophisticated must a robot's sensors be to complete a given task? What are the neccessary conditions? We seek the simplest robots that can complete a task, giving a precise meaning to the idea of simpleness. My goal is to develop a clean, formal technique for comparing robot systems and for studying their ability to complete tasks of varying difficulty. This work draws inspiration from the theory of computation, which plays a similar role in the core of computer science.

Related Publications

J. M. O'Kane and S. M. LaValle. On comparing the power of robots. International Journal of Robotics Research, 27(1):5-23, January 2008.
J. M. O'Kane and S. M. LaValle. Sloppy motors, flaky sensors, and virtual dirt: Comparing imperfect ill-informed robots. IEEE Conference on Robotics and Automation, 2007.
J. M. O'Kane and S. M. LaValle. On Comparing the Power of Mobile Robots. In Proc. Robotics: Science and Systems, 2006.

Localization with Minimal Sensing

Localization, the task of determining a robot's position within its environment, is one of the most important problems for mobile robots. How hard is this problem, in terms of the sensing and motion ability needed to complete it? This work is an investigation of the information requirements of the localization task. We found upper and lower bounds on the boundary between robot models that enable localization and those that do not.

Related Publications

J. M. O'Kane and S. M. LaValle. Localization with limited sensing., IEEE Transactions on Robotics, 23:704-716, August 2007.
J. M. O'Kane. Global localization using Odometry. In Proc. IEEE International Conference on Robotics and Automation, 2006.
J. M. O'Kane and S. M. LaValle. Almost-sensorless localization. In IEEE International Conference on Robotics and Automation, 2005.

Roomba Experiments

Experimental robotics research using Roomba autonomous vacuum cleaner robots. Roombas make an excellent research platform because they are inexpensive and reliable. The challenge is in developing algorithms to deal with uncertainty arising from limited sensing and imprecise motions.

Roomba lab

Related Publications

Lawrence H. Erickson, Joseph Knuth, Jason M. O'Kane, Steven M. LaValle. Probabilistic localization with a blind robot.. To appear in IEEE International Conference on Robotics and Automation, 2008.

Pareto Optimal Coordination

In many situations, teams of robots must interact in a shared workspace. If the robots have distinct goals and objective functions, then the design of collision-free coordination plans for these teams is a multi-objective optimization problem. This work uses the idea of Pareto optimality to generate a set of non-dominated solutions to multiple-robot coordination problems.

Related Publications

R. Ghrist, J. M. O'Kane, and S. M. LaValle. Computing Pareto optimal coordinations on roadmaps. International Journal of Robotics Research, 24(11):997-1010, 2005.
R. Ghrist, J. M. O'Kane, and S. M. LaValle. Pareto optimal coordination on roadmaps. In Proc. Sixth International Workshop on the Algorithmic Foundations of Robotics, 2004.
H. Chitsaz, J. M. O'Kane, and S. M. LaValle. Exact Pareto-optimal coordination of two translating polygonal robots on an acyclic roadmap. In Proc. IEEE International Conference on Robotics and Automation, 2004.