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Optical data constitutes a valuable tool for navigation. Tanner Labs has developed Cartesian Flow, an efficient algorithm for computing optical flow, and demonstrated, using theoretical analysis and empirical simulations, that it can be used for robotic self-navigation in a variety of environments. Then, we implemented Cartesian Flow by developing a reconfigurable computer that offers higher performance than a custom IC.

We based the reconfigurable computer design on our neural network reconfigurable computer; they share the same motherboard and communications infrastructure. Its flexible and expandable format can be easily modified to include more sophisticated optical flow computations or post-flow navigational algorithms (three more daughtercards can be added).

This research has been sponsored by the U.S. Department of Commerce under the SBIR program.

 

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