The Autonomous City Explorer (ACE) project combines research from autonomous outdoor navigation and human-robot interaction. The ACE robot is capable of navigating unknown urban environments without the use of GPS data or prior map knowledge. It finds its way by interacting with pedestrians in a natural and intuitive way and building a topological representation of its surroundings. In a recent experiment the robot managed to successfully travel a 1.5 km distance from the campus of the Technische Universität München to Marienplatz, the central square of Munich.This article describes the principles and system components for navigation in urban environments, information retrieval through natural human-robot interaction, the construction of a suitable semantic representation as well as results from the field experiment.

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 "soft" 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.

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.

This paper presents a novel robotic architecture that is suitable for modular distributed multi-robot systems. The architecture is based on an interface supporting real-time inter-process communication, which allows simple and highly efficient data exchange between the modules. It allows monitoring of the internal system state and easy logging, thus facilitating the module development. The extension to distributed systems is provided through a communication middleware, which enables fast and transparent exchange of data through the network, although without real-time guarantees. The advantages and disadvantages of the architecture are rated using an existing framework for evaluation of robot architectures.

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.