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Demonstrating the Capabilities of MindStorms NXT for the AI Curriculum

Demonstrating the Capabilities of MindStorms NXT for the AI Curriculum. Myles McNally Frank Klassner Alma College Villanova University AAAI Spring Symposium March 26 - 28, 2007. MindStorms Education NXT Base Kit. The NXT (the brains) Three Servo Motors 1 degree sensitivity Sensors

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Demonstrating the Capabilities of MindStorms NXT for the AI Curriculum

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  1. Demonstrating the Capabilities of MindStorms NXT for the AI Curriculum Myles McNally Frank Klassner Alma College Villanova University AAAI Spring Symposium March 26 - 28, 2007

  2. MindStorms Education NXT Base Kit • The NXT (the brains) • Three Servo Motors • 1 degree sensitivity • Sensors • Two Touch Sensors • One Light Sensor • OneSound Sensor • One Sonar Sensor The Base Kit

  3. The NXT Itself • 48MHz ARM7 CPU • 64KB RAM • 256KB flash RAM • Bluetooth communication • USB 2.0 port • 100x64 pixel LCD display • Four sensor ports • Three motor ports • Rechargeable battery pack NXT Brick

  4. Sonar Sensor • Accurate in distances from 6 to 180 centimeters • Objects at distances beyond180 centimeters were not reliably located • Returned distances usually slightly larger than actual distances (1 - 3 cm) • Lateral resolution is very good in a cone of 30 degrees out to 180 centimeters Sonar “Test Bed”

  5. Language Support • Support is emerging • Java • ICommand – a library of remote control classes • LeJOS – still in a very early alpha release • RCXLisp • Frank is working on the port of it • RobotC • Some others…

  6. Two Projects for the AI Course • Occupancy Grid Mapping using the forward model • Monte Carlo Localization • Both projects are solved in the one-dimensional case NXT Robot with Side Facing Sonar

  7. Occupancy Grid Mapping • In this project students use the NXT, equipped with a side facing sonar sensor, to map one side of a “hallway.” • Our approach uses what Thrun calls the Standard Occupancy Grid Mapping Algorithm. The “Hallway” Screen Shot - Real Walls Below, Estimated Walls Above

  8. Monte Carlo Localization • In this project students solve the global localization problem in the 1-d case • Our approach employs the particle filtering approach described by Fox, whose linear example was implemented by Greenwald et al • This approach can easily be adapted to handle the “kidnapped robot” problem

  9. Robot has moved a short distance down the hallway Robot is well localized as it approaches the end of the hallway

  10. In Conclusion… • These projects • Complete details:www.mcs.alma.edu/LMICSE (coming soon) • Paper: To be presented at FLAIRS 2007 • See us for a demonstration! • Attend our NXT workshop • University of Mississippi, June 6-10 2007

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