This video presents the Autonomous City Explorer (ACE) project. Its goal was to create a robot capable of navigating unknown urban environments without the use of GPS data or prior map knowledge. The robot had to find its way solely by interacting with pedestrians and building a topological representation of its surroundings. This video outlines the necessary ingredients for successful low-level navigation on sidewalks, information retrieval from pedestrians as well as the construction of a semantic representation of an urban environment. A system architecture for outdoor localization, traversability assessment, path planning, behavior selection and topological abstraction in urban environments is presented.

One of the greatest challenges nowadays in robotics is the advancement of robots from industrial tools to companions and helpers of humans, operating in natural, populated environments. In this respect, the Autonomous City Explorer (ACE) project aims to combine the research fields of autonomous mobile robot navigation and human robot interaction. A robot has been created that is capable of navigating in an unknown, highly populated, urban environment, based only on information extracted through interaction with passers-by and its local perception capabilities. This paper describes the algorithms and architecture that make up the navigation subsystem of ACE. More specifically, the algorithms used for Simultaneous Localization and Mapping (SLAM), path planning in dynamic environments and behavior selection are presented, as well as the system architecture that integrates them to a complete working system. Results from an extended field experiment, where the robot navigated autonomously through the downtown city area of Munich, are analyzed and show that the robot is capable of long-term, safe navigation in real-world settings.

Autonomous mobile robots are deployed in a variety of application domains, resulting in scenario specific implementations. However these systems share common components responsible for perception, path planning and task execution. In order to find a formal way to identify the influence of the environmental complexity to the used methods, an approach for quantitative system interdependence analysis is introduced. The coherence between several performance indicators of different system components, as well as the influence of environmental parameters on the system, are learned and quantitatively evaluated. Performance evaluation of an autonomous robot navigating in two different urban environments is conducted and presented results demonstrate the applicability of the proposed approach.

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 Rao-Blackwellized 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 domain of the autonomous city explorer robot and results from the implementation on the robotic platform are presented.

In this paper, a control scheme that combines trajectory planning and gaze direction control for robotic exploration is presented. The objective is to calculate the gaze direction and simultaneously plan the trajectory of the robot over a given time horizon, so that localization and map estimation errors are minimized while the unknown environment is explored. Most existing approaches perform a greedy optimization for the trajectory generation only over the next time step and usually neglect the limited field of view of visual sensors and consequently the need for gaze direction control. In the proposed approach an information-based objective function is used, in order to perform multiple step planning of robot motion, which quantifies a trade-off between localization, map accuracy and exploration. Relative entropy is used as an information metric for the gaze direction control. The result is an intelligent exploring mobile robot, which produces an accurate model of the environment and can cope with very uncertain robot models and sensor measurements

As robots are gradually leaving highly structured factory environments and moving into human populated environments, they need to possess more complex cognitive abilities. Not only do they have to operate efficiently and safely in natural populated environments, but also be able to achieve higher levels of cooperation and interaction with humans. The Autonomous City Explorer (ACE) project envisions to create a robot that will autonomously navigate in an unstructured urban environment and find its way through interaction with humans. To achieve this, research results from the fields of autonomous navigation, path planning, environment modeling, and human-robot interaction are combined. In this paper a novel hardware platform is introduced, a system overview is given, the research foci of ACE are highlighted, approaches to the occurring challenges are proposed and analyzed, and finally some first results are presented.