Catálogo de publicaciones - libros

Assistance and service systems are one of the main research topics in robotics today. A major problem for creating these systems is that they have to work and navigate in the real world. Because this world is too complex to model, these robots need to make intelligent decisions and create an intelligent behavior without knowing everything about the current situation. For these aspects, the importance of emotion increases, because the emotional influence helps human beings as well as animals to make their decisions. To enable a robot to use emotions, a concept for an emotion based control architecture was designed. The basis of this architecture is a behavior based approach. This paper presents the developed architecture. Furthermore two application possibilities are presented, where parts of the architecture were already tested and implemented.

One of the most challenging subfields, and a still little researched niche of machine learning, is the inductive synthesis of recursive programs from incomplete specifications, such as examples for the desired input/output behavior [1,2,3,4].

We propose a novel approach to experience collection for autonomous service robots performing complex activities. This approach enables robots to collect data for multiple learning problems at a time, abstract it and transform it into information specific to the learning tasks and thereby speeding up the learning process. The approach is based on the concept of hierarchical hybrid automata, which are used as expressive representational mechanisms that allow for the specification of these experience-related capabilities independent of the program itself.

Dense records of user actions allow an intelligent environment to support its user with an augmented personal memory. In this article, we report on task-oriented user studies concerning mechanisms for sharing such memories, and show how the structure of a social network can be exploited in order to extend the resulting sharing approach.

This paper introduces Semantic Reflection (SR), a design paradigm for intelligent applications which represents applications’ objects and interfaces on a common knowledge representation layer (KRL). SR provides unified knowledge reflectivity specifically important for complex architectures of novel human-machine interface systems.

The paper presents a concept of an intelligent control platform for the Hexor mobile robot, based on the XTT knowledge representation method for rule-based systems. The control systems is implemented in Prolog, with use of the Embedded Prolog Platform. The paper presents real-time control capabilities provided by this solution.

Detecting unknown worms is a challenging task. Extant solutions, such as anti-virus tools, rely mainly on prior explicit knowledge of specific worm signatures. As a result, after the appearance of a new worm on the Web there is a significant delay until an update carrying the worm’s signature is distributed to anti-virus tools. We propose an innovative technique for detecting the presence of an unknown worm, based on the computer operating system measurements. We monitored 323 computer features and reduced them to 20 features through feature selection. Support vector machines were applied using 3 kernel functions. In addition we used active learning as a selective sampling method to increase the performance of the classifier, exceeding above 90% mean accuracy, and for specific unknown worms 94% accuracy.

This paper presents the behaviour-based control architecture iB2C (ntegrated ehaviour-ased ontrol) used for the development of complex robotic systems. The specification of behavioural components is described as well as the integration of behaviour coordination and hierarchical abstraction. It is considered how the design process can be supported by guidelines and by tools for development as well as analysis. Finally some application platforms are presented.

Many modern control systems, e.g., in automotive or robotic applications get increasingly complex and hard to design. This is due to the complex interactions of their internal subsystems, but additionally, these systems operate in a dynamically changing, complex environment. The Organic Computing (OC) initiative tries to cope with the resulting engineering demands by introducing emergence and self-x properties into the systems (e.g., self-organization, self-optimization). Within this context, we focus on control systems which adapt their behavior autonomously by learning.

This paper presents a project that is developing a mobile service robot to assist users in biological and pharmaceutical laboratories by executing routine jobs such as filling and transporting microplates. A preliminary overview of the design of the mobile platform with a robotic arm is provided. Moreover, the approaches to localization and intuitive multimodal human-machine interaction using speech and touchpad input are described. One focus of the project is aspects of safety since the robot and humans will share a common environment.