Vision-based maze navigation for humanoid robots

We present a vision-based approach for navigation of humanoid robots in networks of corridors connected through curves and junctions. The objective of the humanoid is to follow the corridors, walking as close as possible to their center to maximize motion safety, and to turn at curves and junctions. Our control algorithm is inspired by a technique originally designed for unicycle robots that we have adapted to humanoid navigation and extended to cope with the presence of turns and junctions. The corridor following control law is here proven to provide asymptotic convergence of robot heading and position to the corridor bisector even when the corridor walls are not parallel. A State Transition System is designed to allow navigation in mazes of corridors, curves and T-junctions. Extensive experimental validation prove the validity and robustness of the approach, and in particular the successful extension of the controller to turns and junctions.