The overall performance of a robotic system is commonly expressed by a single scenario-specific metric which is supposed to be optimized. However, the metric describing the performance of a single subtask within a scenario may be different. Nevertheless, the scenario performance is most likely dependent on the subtask performances but a mutual transformation is not straightforward in general, especially in complex robotic systems. This leads to what we call the common pricing problem, i.e. the problem to determine the functional relationship among a set of different performance criteria and then account for this relationship in the various optimizations throughout all system layers. In this paper we present an approach to first learn a probabilistic model of the metric interdependencies, and thereafter utilize this model for performance estimation and optimal task parameterization during planning and execution respectively. The proposed method is validated in a simulation.

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.

Robots acting in populated environments must be capable of safe but also time efficient navigation. Trying to completely avoid regions resulting from worst case predictions of the obstacle dynamics may leave no free space for a robot to move, especially in environments with high dynamic. This work presents an algorithm for a ldquosoftrdquo risk mapping of dynamic objects leaving the complete space free of static objects for path planning. Markov Chains are used to model the dynamics of moving persons and predict their potential future locations. These occlusion estimations are mapped into risk regions which serve to plan a path through potentially obstructed space searching for the trade-off between detour and time delay. The offline computation of the Markov Chain model keeps the computational effort low, making the approach suitable for online applications.