In this paper, wide-field integration methods, which are inspired by the spatial decompositions of wide-field patterns of optic flow in the insect visuomotor system, are explored as an efficient means to extract visual cues for guidance and navigation. A control-theoretic framework is developed and used to quantitatively link weighting functions to behaviorally-relevant interpretations such as relative orientation, position, and speed in a corridor environment. It is shown through analysis and demonstration on a ground vehicle that the proposed sensorimotor architecture gives rise to navigational heuristics, namely, centering and speed regulation, which are observed in natural systems.

Wide-field integration (WFI) methods, which are based on the spatial decompositions of optic flow performed by motion sensitive tangential neurons within the insect visuomotor system, have been shown to be an efficient and robust way to extract visual cues for guidance and navigation. In this paper, previous analytical results on WFI-based autonomous local guidance are validated on a wheeled robotic platform. Successful corridor navigation is achieved through closed loop feedback of the first two spatial harmonics of the optic flow, which provide the lateral and rotational stiffness with respect to local obstacles required to stabilize a collision-free trajectory through the environment.