Active Vision and Feature Selection in Evolutionary Behavioral Systems
We describe an evolutionary approach to active vision systems for dynamic feature selection. After summarizing recent work on evolution of a simulated active retina for complex shape discrimination, we describe in detail experiments that extend this approach to an all-terrain mobile robot equipped with a mobile camera. We show that evolved robots are capable of selecting simple visual features and actively maintaining them on the same retinal position, which largely simplifies the "recognition'' task, in order to generate efficient navigation trajectories with an extremely simple neural control system. Analysis of evolved solutions indicates that robots develop a simple and yet very efficient version of edge detection and visual looming to detect obstacles and move away from them. Two evolved sensory-motor strategies are described, one where the mobile camera is actively used throughout the entire navigation and one where it is used only at the beginning to point towards relevant environmental features. The relationship between these two strategies are discussed in the context of the underlying visuo-motor mechanisms and of the evolutionary conditions.