O'Kane

Professor O'Kane's research is in planning algorithms for robotics and autonomous systems. As robot technology becomes more practical, it becomes increasingly important to design robots that are suitable for domains that are unpredictable and inhospitable, while ensuring that the resulting systems are robust and inexpensive. Because sensing and uncertainty are central issues in robotics, it is essential to understand how to solve robotics problems when sensing is limited and uncertainty is great. Professor O'Kane's interests span sensor-based algorithmic robotics and related areas, including planning under uncertainty, artificial intelligence, computational geometry, sensor networks, and motion planning.

Education

• Ph. D., University of Illinois (2007)
• M. S., University of Illinois (2005)
• B. S., Taylor University (2001)

Selected Publications

• Jason M. O'Kane, Steven M. LaValle. On comparing the power of robots. International Journal of Robotics Research, 27(1):5--23, January 2008.
• Jason M. O'Kane, Steven M. LaValle. Localization with limited sensing. IEEE Transactions on Robotics, 23:704--716, August 2007.
• Jason M. O'Kane, Benjamin Tovar, Peng Cheng, Steven M. LaValle. Algorithms for Planning Under Uncertainty in Prediction and Sensing. In S. S. Ge and F. L. Lewis, editors, Autonomous Mobile Robots: Sensing, Control, Decision-Making, and Applications, Series in Control Engineering, chapter 13, pages 501--547. Marcel Dekker, 2006.
• Jason M. O'Kane, Steven M. LaValle. Sloppy motors, flaky sensors, and virtual dirt: Comparing imperfect ill-informed robots. In Proc. IEEE International Conference on Robotics and Automation, 2007.