Omnidirectional Active Vision in Evolutionary Car Driving

Perception in intelligent systems is closely coupled with action and the actual environment the system is situated in. Embodied robots exploit by means of sensory-motor coordination the environment in simplifying a complex visually guided task, yielding successful robust perceptual behavior. This active vision approach enables robots to sequentially and interactively select and analyze only the task-relevant parts of the total available visual scene. In this study, active vision and feature selection operating on an omnidirectional visual scene are coevolved in simulation by a genetic algorithm, yielding neural controllers of a robotic scale car equipped with an omnidirectional camera that is capable of driving two differently shaped circuits at high-speed without going off-road. Successfully evolved individuals show the sophisticated strategies of an artificial retina selecting quickly only the task-relevant features in the accessible information-rich visual scene provided by the omnidirectional camera. The evolved behaviors of the robotic car and the corresponding strategies of its retina are analyzed, and additionally the obtained results from the car equipped with the omnidirectional camera are compared with those from the car equipped with a standard pan-tilt camera. Finally, the advantages of the used active vision approach operating on an omnidirectional camera are discussed.