Kai Häussermann

RoboEarth: connecting robots worldwide

In this paper, we present the core concept and the benefits of an approach called RoboEarth which will be highly beneficial for future robotic applications in science and industry. RoboEarth is a world-wide platform which robots can use to exchange position and map information as well as task-related, hardware-independent action recipes. This will enable manufacturers worldwide to break down their costs and efforts for reproducing software algorithms for robot behavior over and over again. The RoboEarth framework can store all relevant data from algorithms to complex behavior descriptions what allows robots to act autonomously in an unknown, unspecified environment. Especially in the field of interaction with humans RoboEarth can bring forward the behavior of robots and simplify the software design for developers in that field.

Creating and using RoboEarth object models

This paper presented an approach to create 3D object models for robotic and vision applications in a fast and inexpensive way compared to established approaches. By using the RoboEarth system for storing the created object models users have world-wide access to the data and can immediately reuse a model as soon as it was created and uploaded. The approach shows general applicability for different kinds of cameras. In this work this was shown by two example implementations for the recognition process of objects. The quality of the recognition can be verified in the video. Combined with the knowledge saved in the RoboEarth database the objects can also be properly classified.

A System for Distributed Context Reasoning

Context aware systems use context information toadapt their behaviour accordingly. In order to derive highlevel context information from low level context, such as sensorvalues, context reasoning methods that correlate observablecontext information, are necessary. Several context reasoningmechanisms have been proposed in the literature. Usually thesemechanisms are centralized, leading to suboptimal utilization ofnetwork resources and poor system performance in case of largescalescenarios. Therefore to increase the scalability of contextreasoning systems the development of methods that distribute thereasoning process is necessary. Existing distributed approachesare method specific and do not provide a generic formalizationfor distributed reasoning. In this paper we introduce a novelsystem which enables distributed context reasoning in a genericway that is independent of the reasoning algorithm.

Supervised learning algorithm for automatic adaption of situation templates using uncertain data

In this paper a learning algorithm for the automatic adaption of a situation template is presented. The approach strongly relies on human-machine interaction as user feedback is a substantial part to automatically adapt a global knowledgebase in this case. The work bases on the assumption of uncertain data and includes elements from the domain of Bayesian Networks and Machine Learning. It is embedded into the cluster of excellence Nexus at the University of Stuttgart which has the aim to build a distributed context aware user-friendly system for sharing context data.

Extended TA Algorithm for Adapting a Situation Ontology

In this work we introduce an improved version of a learning algorithm for the automatic adaption of a situation ontology (TAA) [1] which extends the basic principle of the learning algorithm. The approach bases on the assumption of uncertain data and includes elements from the domain of Bayesian Networks and Machine Learning. It is embedded into the cluster of excellence Nexus at the University of Stuttgart which has the aim to build a distributed context aware system for sharing context data.

Event based distributed real-time communication architecture for multi-agent systems

In this paper an event-based distributed real-time communication architecture for multi-agent systems is presented. The architecture was designed regarding the needs of hardware-based multi-agent systems, especially robotic systems. This includes in particular fast and reliable transportation of messages in unstable wireless networks. Furthermore the architecture is usable for different agent-based software systems and allows communication between different systems even if they have a complete different representation of internal data. The architecture was evaluated in the RoboCup scenario with a multi-agent system of 5 robots interacting in a highly dynamic environment. Using this system, the RoboCup team of the University of Stuttgart won the RoboCup German Open Championship and World Championship in 2009.

Advanced Data Logging in RoboCup

In this work an advanced data logging approach for the RoboCup domain using an autonomous camera man is presented. It includes approaches from the field of real-time robot message logging, situation based camera control and 3D video visualisation. Based on the implementation of this work the RoboCup team of the IPVS of the University of Stuttgart won the first price in the scientific challenge of the RoboCup World Championship 2009 in Graz.

A Novel Framework for Anomaly Detection of Robot Behaviors

Autonomous mobile robots are designed to behave appropriately in changing real-world environments without human intervention. In order to satisfy the requirements of autonomy, robots have to cope with unknown settings and several issues of uncertainties in dynamic, unstructured and complex environments. A first step is to provide a robot with cognitive capabilities and the ability of self-examination to detect behavioral abnormalities. Unfortunately, most existing anomaly detection systems are neither suitable for the domain of robotic behavior nor flexible enough or even well generalizable. In the following article, we introduce a novel anomaly detection framework based on spatial-temporal models for robotic behaviors which is generally applicable for e.g., plan execution monitoring. The introduced framework combines the methodology of Kohonen’s Self-organizing Maps (SOMs) and Probabilistic Graphical Models (PGM) exploiting all advantages of both concepts. The underlying methods of the framework are discussed briefly, whereas the data-driven training of the spatial-temporal model and the reasoning process are described in detail. Finally, the framework is evaluated with different scenarios to emphasize its potential and its high level of generalization and flexibility in robotic application.

An Adaptive Fuzzy Based System for Time Critical Real World Applications

From a control theory point of view, robotics and artificial intelligence offer exceptionally complex problems. Often the examined control systems have a high number of inputs and outputs, show a black-box behaviour and can not be systematically analyzed due to a vague output and long reaction times. Adaptive behaviour control and enhancement during runtime of robots moving with high speed is such a problem, with the added requirement of real-time capability. In this paper, an adaptive pre-calculated fuzzy system is proposed as a possible solution for this task. The basic structure, construction process and adaption mechanism are described, furthermore the runtime for various dimensions is benchmarked as efficiency aspects are a major contribution of the approach.

Recognizing Hardware Faults on Mobile Robots Using Situation Analysis Techniques

Current research in the field of robotics deals with planning and executing of increasingly complex tasks as well as applications in which autonomous mobile robot systems interact with highly dynamic environments. In order to operate successfully in a highly dynamic physical world a mobile robot needs awareness to adapt its behavior according to the environment. This awareness also has to cope with uncertainty due to imperfect hardware sensors and actuators or network delays of the robot itself. Thus another important ability of a mobile robot is to ”understand” its internal state to detect any kind of hardware faults in the system. In this work a highly customizable framework for situation awareness is presented. For the evaluation of this framework we take advantage of its generality and assume fault diagnosis as a more specific subclass of the general situation awareness process to extend the situation detection framework for fault detection and diagnosis in a multi robot system.