An Active SLAM Approach for Autonomous Navigation of Nonholonomic Vehicles.
Eduardo Lopez, Caleb De Bernardis, Tomás Martinez-Marín · 2011
In this paper we propose a new approach for active SLAM (Simultaneous Localization And Mapping) of nonholonomic vehicles. Both the environment and the vehicle model are unknown in advance, thus the path planner uses reinforcement learning to acquire the vehicle model, which is estimated by a reduced set of transitions. At the same time, the vehicle explores the environment creating a consistent map through optimal path motion. The mapping is represented by sets of ordered and weighted data points, named objects, that provide some advantages with respect to conventional methods. In order to guide the navigation and to build a map of the environment the planner employs a three-dimensional controller based on the concept of virtual wall following. Both simulation and experimental results are reported to show the satisfactory performance of the method.