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One of the biggest challenges of robotics is to create systems capable of operating efficiently and safely in natural, populated environments. This way, robots can evolve from tools performing well-defined tasks in structured industrial or laboratory settings, to integral parts of our everyday lives. However such systems require complex cognitive capabilities, to achieve higher levels of cooperation and interaction with humans, while coping with rapidly changing objectives and environments. In order to address these challenges a robot capable of autonomously exploring densely populated urban environments, is created within the Autonomous City Explorer (ACE) project (Lidoris et al., 2007). To be truly autonomous such a system must be able to create a model of its unpredictable dynamic environment based on noisy sensor information and reason about it. More specifically, a robot is envisioned that is able to find its way in an urban area, without a city map or GPS. In order to find its target, the robot will approach pedestrians and ask for directions. Due to sensor limitations the robot can observe only a small part of its environment and these observations are corrupted by noise. By integrating successive observations a map can be created, but since also the motion of the robot is subject to error, the mapping problem comprises also a localization problem. This duality constitutes the Simultaneous Localization And Mapping (SLAM) problem. In dynamic environments the problem becomes more challenging since the presence of moving obstacles can complicate data association and lead to incorrect maps. Moving entities must be identified and their future position needs to be predicted over a finite time horizon. The autonomous sensory-motor system is finally called to make use of its self-acquired uncertain knowledge to decide about its actions. A Bayesian framework that enables recursive estimation of a dynamic environment model and action selection based on these uncertain estimates is introduced. This is presented in Section 2. In Section 3, it is shown how existing methods can be combined to produce a working implementation of the proposed framework. A Rao-Blackwellized particle filter (RBPF) is deployed to address the SLAM problem and combined with recursive conditional O pe n A cc es s D at ab as e w w w .in te ch w eb .o rg

Bayesian Framework for State Estimation and Robot Behaviour Selection in Dynamic Environments