Ein fundamentaler Schritt des fallbasierten Schliessens (FBS) ist das Retrieval einer handhabbaren Menge von Fällen aus einer Fallbasis, die als Grundlage für die weiteren Schritte des FBS, wie die Modifikation und Übertragung bekannter Lösungen auf einen gegebenen Problemfall, dienen.
Based on experiences from an autonomous mobile robot project called MOBOT -III, we found hard realtime-constraints for the operating-system-design. ALBATROSS is "A flexible multi-tasking and realtime network-operatingsystem-kernel", not limited to mobile- robot-projects only, but which might be useful also wherever you have to guarantee a high reliability of a realtime-system. The focus in this article is on a communication-scheme fulfilling the demanded (hard realtime-) assurances although not implying time-delays or jitters on the critical informationchannels. The central chapters discuss a locking-free shared buffer management, without the need for interrupts and a way to arrange the communication architecture in order to produce minimal protocol-overhead and short cycle-times. Most of the remaining communication-capacity (if there is any) is used for redundant transfers, increasing the reliability of the whole system. ALBATROSS is actually implemented on a multi-processor VMEbus-system.
This paper refers to the problem of adaptability over an infinite period of time, regarding dynamic networks. A never ending flow of examples have to be clustered, based on a distance measure. The developed model is based on the self-organizing feature maps of Kohonen ,  and some adaptations by Fritzke . The problem of dynamic surface classification is embedded in the SPIN project, where sub-symbolic abstractions, based on a 3-d scanned environment is being done.
The problem to be discussed here, is the usage of neural network clustering techniques on a mobile robot, in order to build qualitative topologic environment maps. This has to be done in realtime, i.e. the internal world model has to be adapted by the flow of sensor- samples without the possibility to stop this data-flow.Our experiments are done in a simulation environment as well as on a robot, called ALICE.
Based on the experiences from an autonomous mobile robot project called MOBOT-III, we found hard realtime-constraints for the operating- system-design. ALBATROSS is "A flexible multi-tasking and realtime network-operating-system-kernel". The focusin this article is on a communication-scheme fulfilling the previous demanded assurances. The centralchapters discuss the shared buffer management and the way to design the communication architecture.Some further aspects beside the strict realtime-requirements like the possibilities to control and watch a running system, are mentioned. ALBATROSS is actually implemented on a multi-processor VMEbus-system.
Based on the idea of using topologic feature-mapsinstead of geometric environment maps in practical mobile robot tasks, we show an applicable way tonavigate on such topologic maps. The main features regarding this kind of navigation are: handling of very inaccurate position (and orientation) information as well as implicit modelling of complex kinematics during an adaptation phase. Due to the lack of proper a-priori knowledge, a re-inforcement based model is used for the translation of navigator commands to motor actions. Instead of employing a backpropagation network for the cen-tral associative memory module (attaching actionprobabilities to sensor situations resp. navigatorcommands) a much faster dynamic cell structure system based on dynamic feature maps is shown. Standard graph-search heuristics like A* are applied in the planning phase.
The problem to be discussed in this paper may be characterized in short by the question: "Are these two surface fragments belonging together (i.e. belonging to the same surface)?" The presented techniques try to benefit from some predefined knowledge as well as from the possibility to refine and adapt this knowledge according to a (changing) real environment, resulting in a combination of fuzzy-decision systems and neural networks. The results are encouraging (fast convergence speed, high accuracy), and the model might be used for a wide range of applications. The general frame surrounding the work in this paper is the SPIN- project, where emphasis is on sub-symbolic abstractions, based on a 3-d scanned environment.
World models for mobile robots as introduced in many projects, are mostly redundant regarding similar situations detected in different places. The present paper proposes a method for dynamic generation of a minimal world model based on these redundancies. The technique is an extention of the qualitative topologic world modelling methods. As a central aspect the reliability regarding errortolerance and stability will be emphasized. The proposed technique demands very low constraints on the kind and quality of the employed sensors as well as for the kinematic precision of the utilized mobile platform. Hard realtime constraints can be handled due to the low computational complexity. The principal discussions are supported by real-world experiments with the mobile robot "
This article will discuss a qualitative, topological and robust world-modelling technique with special regard to navigation-tasks for mobile robots operating in unknownenvironments. As a central aspect, the reliability regarding error-tolerance and stability will be emphasized. Benefits and problems involved in exploration, as well as in navigation tasks, are discussed. The proposed method demands very low constraints for the kind and quality of the employed sensors as well as for the kinematic precision of the utilized mobile platform. Hard real-time constraints can be handled due to the low computational complexity. The principal discussions are supported by real-world experiments with the mobile robot
Self-localization in unknown environments respectively correlation of current and former impressions of the world is an essential ability for most mobile robots. The method,proposed in this article is the construction of a qualitative, topological world model as a basis for self-localization. As a central aspect the reliability regarding error-tolerance and stability will be emphasized. The proposed techniques demand very low constraints for the kind and quality of the employed sensors as well as for the kinematic precisionof the utilized mobile platform. Hard real-time constraints can be handled due to the low computational complexity. The principal discussions are supported by real-world experiments with the mobile robot.