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Cellular multiadaptive analogic architecture: a computational framework for UAV applications

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5 Author(s)
Rekeczky, C. ; Analogical & Neural Comput. Lab., Hungarian Acad. of Sci., Budapest, Hungary ; Szatmari, I. ; Balya, D. ; Timar, G.
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An efficient adaptive algorithm in real-time applications should make optimal use of the available computing power for reaching some specific design goals. Relying on appropriate strategies, the spatial resolution/temporal rate can be traded against computational complexity; and sensitivity traded against robustness, in an adaptive process. In this paper, we present an algorithmic framework where a spatial multigrid computing is placed within a temporal multirate structure, and at each spatial grid point, the computation is based on an adaptive multiscale approach. The algorithms utilize an analogic (analog and logic) architecture consisting of a high-resolution optical sensor, a low-resolution cellular sensor-processor and a digital signal processor. The proposed framework makes the acquisition of a spatio-temporally consistent image flow possible even in case of extreme variations (relative motion) in the environment. It ideally supports the handling of various difficult problems on a moving platform including terrain identification, navigation parameter estimation, and multitarget tracking. The proposed spatio-temporal adaptation relies on a feature-based optical-flow estimation that can be efficiently calculated on available cellular nonlinear network (CNN) chips. The quality of the adaptation is evaluated compared to nonadaptive spatio-temporal behavior where the input flow is oversampled, thus resulting in redundant data processing with an unnecessary waste of computing power. We also use a visual navigation example recovering the yaw-pitch-roll parameters from motion-field estimates in order to analyze the adaptive hierarchical algorithmic framework proposed and highlight the application potentials in the area of unmanned air vehicles.

Published in:

Circuits and Systems I: Regular Papers, IEEE Transactions on  (Volume:51 ,  Issue: 5 )

Date of Publication:

May 2004

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