Snake-like robots have gained popularity in the last three decades for their ability to utilize several gaits in order to navigate through di erent terrains. They are analogous in morphology to snakes, tentacles, and elephant trunks. We propose a novel method of navigating a snake-like robot based on the Harmonic Field with Optimized Boundary Conditions (HFOBC) and a boundary following algorithm. We apply the HFOBC navigation function using a number of fictitious charges equally spaced on each link. These charges actively follow the potential field towards the target. Futhermore, a generalized mathematical model for an n-link snake-like robot based on Lagrange formulation has also been proposed in this paper.
In this paper, we propose two techniques for face recognition, namely, view-based and biometric-based face recognition. Both use General Backpropagation Neural Networks (GBPN's). In the view-based method, we extract sub-images of the eyes, the nose, and the mouth and feed them into a GBPN. In the biometric-based method, seven measurements of the face will be fed into another GBPN. We illustrate the results of the proposed algorithms by applying them on the Cambridge ORL face database, which contains quite a high degree of variability in expression, pose, and facial details. We have found that the view-based method outperforms the biometricbased method. Thus, we have selected the viewbased method to function as the main neural network whereas the biometric-based method will function as a supportive neural network.2007 IEEE International Conference on Granular Computing 0-7695-3032-X/07 $25.00
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