Obstacle detection by recognizing binary expansion patterns

A technique is described for obstacle detection, based on the expansion of the image-plane projection of a textured object, as its distance from the sensor decreases. Information is conveyed by vectors whose components represent first-order temporal and spatial derivatives of the image intensity, which are related to the time to collision through the local divergence. Such vectors may be characterized as patterns corresponding to “safe” or “dangerous” situations. We show that the essential information is conveyed by single-bit vector components, representing the signs of the relevant derivatives. We use two recently developed, high capacity classifiers, employing neural learning techniques, to recognize the imminence of collision from such patterns     

A bird's eye view on the descent trajectory

The image of a flat patch of ground, as seen by a descending observer, can be made locally stationary by choice of the local descent angle. This defines a descent trajectory which maintains a motion contrast between the image of a three-dimensional target and that of its background     

Information Approach to Fixed-Gain Design

A method for designing fixed-gain controllers and filters for systems with large parameter variation is presented. The approach is based on finding the minimax point of the Kullback information measure between the fixed-gain system and the optimal system at a given operating point. The effectiveness of the proposed approach is illustrated by designing a fixed-gain system for the short-period control of a high-performance aircraft and evaluating its performance over the flight envelope.