Bayesian state estimation and behavior selection for autonomous robotic exploration in dynamic environments

— In order to be truly autonomous, robots that operate in natural, populated environments must have the ability to create a model of these unpredictable dynamic environments and make use of this self-acquired uncertain knowledge to decide about their actions. A formal Bayesian framework is introduced, which enables recursive estimation of a dynamic environment model and action selection based on this estimate. Existing methods are combined to produce a working implementation of the proposed framework. A RaoBlackwellized particle filter (RBPF) is deployed to address the Simultaneous Localization And Mapping (SLAM) problem and combined with recursive conditional particle filters in order to track people in the vicinity of the robot. In this way, a complete model is provided, which is utilized for selecting the actions of the robot so that its uncertainty is kept under control and the likelihood of achieving its goals is increased. All developed algorithms have been applied to the dom...

• Type: Conference
• Year: 2008
• Where: IROS
• Authors: Georgios Lidoris, Dirk Wollherr, Martin Buss
• Keywords: Dynamic Environment, IROS 2008, Particle filters, Robotics, Unpredictable Dynamic Environments