Autonomous Navigation in Three-Dimensional Urban Environments Using Wide-Field Integration of Optic Flow

摘要

In this paper, a control theoretic framework is introduced to analyze an information extraction approach fromnpatterns of optic flow based on analogs to wide-field motion-sensitive interneurons in the insect visuomotor system.nAn algebraic model of optic flow is developed, based on a parameterization of three-dimensional urbannenvironments. It is shown that estimates of proximity and speed, relative to these environments, can be extractednusing weighted summations of the instantaneous patterns of optic flow. Small perturbation techniques are thennapplied to link weighting patterns to outputs, which are applied as feedback to facilitate stability augmentation andnperform local obstacle avoidance and terrain following. Additive noise and environment uncertainties arenincorporated into an offline procedure for determination of optimal weighting functions. Stability is proven via localnasymptotic analysis and the resulting approach demonstrated in simulation using a micro helicopter in a threedimensionalnurbanlike environment.