To perform a wide range of tasks service robots need to robustly extract knowledge about the world from the data perceived through the robot's sensors even in the presence of varying context-conditions. This makes the design and development of robot perception architectures a challenging exercise. In this paper we propose a robot perception architecture which enables to select and execute at runtime different perception graphs based on monitored context changes. To achieve this the architecture is structured as a feedback loop and contains a repository of different perception graph configurations suitable for various context conditions.

We present the design and development of a benchmarking testbed for the Factory of the Future. The testbed as a physical installation enables to study, compare and assess robotics scenarios involving the integration of mobile robots and manipulators with automation equipment, large-scale integration of service robots and industrial robots, cohabitation of robots and humans, and cooperation of multiple robots and/or humans. We also report on the lessons learned of using the testbed in recent robotic competitions.

Grasping objects and using them in a task-oriented manner is challenging for a robot. It requires an understanding of the object, the robot's capabilities, and the task to be executed. We argue that an explicit representation of these domains increases reusability and robustness of the resulting system. We present the declarative Grasp Domain Definition Language (GDDL) which enables the explicit grasp-planner-independent specification of grasping problems. The formal model underlying GDDL enables the definition of formal constraints that are validated during design time and run time. Our approach has been realized on two real robots.

Deploying a complex robot software architecture on real robot systems and getting it to run reliably is a challenging task. We argue that software deployment decisions should be separated as much as possible from the core development of software functionalities. This will make the developed software more independent of a particular hardware architecture (and thus more reusable) and allow it to be deployed more flexibly on a wider variety of robot platforms. This paper presents a domain-specific language (DSL) which supports this idea and demonstrates how the DSL is used in a model-driven engineering-based development process. A practical example of applying the DSL to the development of an application for the KUKA youBot platform is given.

Most of the recent robot software frameworks follow a component-oriented development approach. They allow developers to compose a distributed application from a set of interacting components. Though these frameworks provide rich functionality, often they fail to cope with non-functional aspects (e.g., network scalability, predictability of system behavior) involved in system design, especially in distributed settings. This research sets out to address aforementioned quality attributes by introducing a pragmatic model, Protocol Stack View (PSV), for the analysis of distributed robotic software. The model relies on the fact that a distributed software can be viewed in terms of three main elements: components, ports and connectors. It specifically focuses on structure and semantics of software connectors on the implementation level. To prove effectiveness and usefulness of PSV a set of experiments were conducted to analyze scalability and to determine the configurable elements that affect it. The experiments are based on the comparison of communication infrastructure provided by two existing software packages, namely ROS and ZeroMQ.

In most behavior-based approaches, implementing a broad set of different behavioral skills and coordinating them to achieve coherent complex behavior is an error-prone and very tedious task. Concepts for organizing reactive behavior in a hierarchical manner are rarely found in behavior-based approaches, and there is no widely accepted approach for creating such behavior hierarchies. Most applications of behavior-based concepts use only few behaviors and do not seem to scale well. Reuse of behaviors for different application scenarios or even on different robots is very rare, and the integration of behavior-based approaches with planning is unsolved. This paper discusses the design, implementation, and performance of a behavior framework that addresses some of these issues within the context of behavior-based and hybrid robot control architectures. The approach presents a step towards more systematic software engineering of behavior-based robot systems.

The ability to autonomously adapt to variations in the environmental conditions is a very useful feature for mobile robots. Of particular interest in robotic soccer are self-calibrating vision systems that automatically adapt to local lighting conditions. The paper presents a method for autonomous calibration of a color classificator used for color blob-based image segmentation and landmark and object recognition. The experimental results demonstrate significantly improved robustness of visual processing.

Developing software for mobile robot applications is a tedious and error-prone task. Modern mobile robot systems are distributed systems, and their designs exhibit large heterogeneity in terms of hardware, operating systems, communications protocols, and programming languages. Vendor-provided programming environments have not kept pace with recent developments in software technology. Also, standardized modules for certain robot functionalities are beginning to emerge. Furthermore, the seamless integration of mobile robot applications into enterprise information processing systems is mostly an open problem. We suggest the construction and use of object-oriented robot middleware to make the development of mobile robot applications easier and faster, and to foster portability and maintainability of robot software. With Miro, we present such a middleware, which meets the aforementioned requirements and has been ported to three different mobile platforms with little effort. Miro also provides generic abstract services like localization or behavior engines, which can be applied on different robot platforms with virtually no modifications.

RoboCup is an increasingly successful attempt to promote the full integration of robotics and AI research. The most prominent feature of RoboCup is that it provides the researchers with the opportunity to demonstrate their research results as a form of competition in a dynamically changing hostile environment, defined as the international standard game definition, in which the gamut of intelligent robotics research issues are naturally involved. The article describes what we have learned from the past RoboCup activities, and overview the future perspectives of RoboCup in the next century, mainly focusing on the real robot leagues. Finally, we introduce the new leagues, one of which will have been held at RoboCup-99 in Stockholm.