Robotics: control, sensing, vision, and intelligence
Robotics: control, sensing, vision, and intelligence
Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence
Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence
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Tactile sensing is advantageous for the acquisition of local, proximal information such as the contact condition between a finger and an object. This type of sensing, however, is not suited for recognizing an entire object that is easily recognized by vision. The objective of this paper is to ease the limitations experienced in tactile sensing by using both a neural model based on the human tactile sensation and a tactile-oriented associative memory model to enable a robot to recognize object contours. In the model, first the direction vectors belonging to segments of the object contour are obtained from a filtered tactile pattern of the simulated neurons' excitation. Second, the vectors are quantized by the chain-symbolizing method and stored for use in a memory matrix that accumulates matrix-products between the vector and its transposition. In the recalling process, complete vectors are remembered even if some input vector elements are missing. In the experiments, a robotic manipulator equipped with a tactile sensor traces five types of contours, these being a circle, a square, a triangle, a star, and a hexagon. After the robot recalls the complete contours, it is able to recognize a complete contour by just touching even a part of a contour.