Cellular Multi-adaptive Analogic Computing: Algorithms for UAV Applications

Abstract

An efficient adaptive algorithm in process real-time applications should make optimal use of the available computing power for reaching specific design goals. Relying on appropriate strategies, spatial resolution/temporal rate can be traded against computational complexity; and sensitivity traded against robustness in an adaptive process. In this work, we present an algorithmic framework where spatial multi-grid computing is placed within a temporal multi-rate structure, and at each spatial grid point, the computation is based on an adaptive multi-scale approach. The algorithms utilize an analogic (analog and logical) architecture consisting of a high-resolution optical sensor, a low-resolution cellular sensorprocessor (cellular nonlinear network – CNN - based chip) and a digital signal processor. The proposed framework makes the acquisition of a spatially and temporally consistent image flow possible even in case of extreme variations in the environment. It ideally supports the handling of difficult problems on a moving platform such as terrain identification, navigation parameter estimation and multi-target tracking. The proposed spatio-temporal adaptation relies on a feature based optical flow estimation that can be efficiently calculated on available CNN chips. The quality of the adaptation is evaluated compared to non-adaptive spatio-temporal behavior (when the input flow is over-sampled and therefore results in redundant data processing with the unnecessary wasting of computing power). We demonstrate how multi-channel visual flow analysis and a classifier driven visual attention-selection mechanism can be efficiently supported by an analogic architecture. We also use a visual navigation example for recovering the yaw-pitch-roll parameters from motion field estimates in order to analyze the proposed adaptive algorithmic framework. The experiments performed on an analogic CNN hardware prototype will highlight the application potentials for unmanned air vehicle (UAV) applications.