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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 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 • Two Touch Sensors • One Light Sensor • OneSound Sensor • One Sonar Sensor The Base Kit
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
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”
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…
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
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
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
Robot has moved a short distance down the hallway Robot is well localized as it approaches the end of the hallway
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