Ben Tribelhorn

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— This paper presents the iRobot corporation's Roomba vacuum as a low-cost resource for robotics research and education. Sensor and actuation models for unmodified Roombas are presented in the context of both special-and general-purpose spatial-reasoning algorithms, including Monte Carlo Localization and FastSLAM. Further tests probe the feasibility of(More)
This paper reports on our experience teaching introductory programming by means of real-world data analysis. We have found that students can be motivated to learn programming and computer science concepts in order to analyze DNA, predict the outcome of elections, detect fraudulent data, suggest friends in a social network, determine the authorship of(More)
This paper overviews the hardware and software components of a robot whose computational engine uses only commodity laptop computers. Web cameras, sonar, and an inexpensive laser-pointer-based ranger provide sensing. By shifting emphasis from engineering precision to computational heft, the robot succeeded at several tasks in the 2005 AAAI scavenger hunt.(More)
This paper investigates the suitability of iRobot's Roomba as a low-cost robotic platform for use in AI research and education. Examining the sensing and ac-tuation capabilities of the vacuum base led us to develop sensor and actuation models more accurate than those provided by the raw API. We validate these models with implementations of Monte Carlo(More)
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