Biologically-inspired visual landmark learning and navigation for mobile robots

Presents a biologically-inspired method for navigating using visual landmarks which have been self-selected within natural environments. A landmark is a region of the grabbed image which is chosen according to its reliability measured through a phase (turn back and look) that mimics the behavior of some social insects. From the self-chosen landmarks suitable navigation information can be extracted following a well known model introduced in biology to explain the bee's navigation behavior. The landmark selection phase affects the conservativeness of the navigation vector field thus allowing us to explain the navigation model in terms of a visual potential function which drives the navigation to the goal. The experiments have been performed using a Nomad200 mobile robot equipped with monocular color vision.