Visual route navigation using an adaptive extension of Rapidly-exploring Random Trees

This paper proposes an adaptive and probabilistic extension of Rapidly-exploring Random Tree (RRT) for visual route navigation of a mobile robot. Using measurements from cameras and infrared range sensors, a temporary local map is built probabilistically with Gaussian processes and adaptively to the change of the route curvature. Based on the probabilistic map, RRT searches the most robust and efficient local path with the probability of collision, and the robot is controlled along the selected path. The performance of the proposed method was verified by reducing not only centering error and standard deviation in simulations but also travel time in real experiments.