Kaiserslautern - Fachbereich Informatik
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Since their invention in the 1980s, behaviour-based systems have become very popular among roboticists. Their component-based nature facilitates the distributed implementation of systems, fosters reuse, and allows for early testing and integration. However, the distributed approach necessitates the interconnection of many components into a network in order to realise complex functionalities. This network is crucial to the correct operation of the robotic system. There are few sound design techniques for behaviour networks, especially if the systems shall realise task sequences. Therefore, the quality of the resulting behaviour-based systems is often highly dependant on the experience of their developers.
This dissertation presents a novel integrated concept for the design and verification of behaviour-based systems that realise task sequences. Part of this concept is a technique for encoding task sequences in behaviour networks. Furthermore, the concept provides guidance to developers of such networks. Based on a thorough analysis of methods for defining sequences, Moore machines have been selected for representing complex tasks. With the help of the structured workflow proposed in this work and the developed accompanying tool support, Moore machines defining task sequences can be transferred automatically into corresponding behaviour networks, resulting in less work for the developer and a lower risk of failure.
Due to the common integration of automatically and manually created behaviour-based components, a formal analysis of the final behaviour network is reasonable. For this purpose, the dissertation at hand presents two verification techniques and justifies the selection of model checking. A novel concept for applying model checking to behaviour-based systems is proposed according to which behaviour networks are modelled as synchronised automata. Based on such automata, properties of behaviour networks that realise task sequences can be verified or falsified. Extensive graphical tool support has been developed in order to assist the developer during the verification process.
Several examples are provided in order to illustrate the soundness of the presented design and verification techniques. The applicability of the integrated overall concept to real-world tasks is demonstrated using the control system of an autonomous bucket excavator. It can be shown that the proposed design concept is suitable for developing complex sophisticated behaviour networks and that the presented verification technique allows for verifying real-world behaviour-based systems.
In robotics, information is often regarded as a means to an end. The question of how to structure information and how to bridge the semantic gap between different levels of abstraction in a uniform way is still widely regarded as a technical issue. Ignoring these challenges appears to lead robotics into a similar stasis as experienced in the software industry of the late 1960s. From the beginning of the software crisis until today, numerous methods, techniques, and tools for managing the increasing complexity of software systems have evolved. The attempt to transfer several of these ideas towards applications in robotics yielded various control architectures, frameworks, and process models. These attempts mainly provide modularisation schemata which suggest how to decompose a complex system into less complex subsystems. The schematisation of representation and information flow however is mostly ignored. In this work, a set of design schemata is proposed which is embedded into an action/perception-oriented design methodology to promote thorough abstractions between distinct levels of control. Action-oriented design decomposes control systems top-down and sensor data is extracted from the environment as required. This comes with the problem that information is often condensed in a premature fashion. That way, sensor processing is dependent on the control system design resulting in a monolithical system structure with limited options for reusability. In contrast, perception-oriented design constructs control systems bottom-up starting with the extraction of environment information from sensor data. The extracted entities are placed into structures which evolve with the development of the sensor processing algorithms. In consequence, the control system is strictly dependent on the sensor processing algorithms which again results in a monolithic system. In their particular domain, both design approaches have great advantages but fail to create inherently modular systems. The design approach proposed in this work combines the strengths of action orientation and perception orientation into one coherent methodology without inheriting their weaknesses. More precisely, design schemata for representation, translation, and fusion of environmental information are developed which establish thorough abstraction mechanisms between components. The explicit introduction of abstractions particularly supports extensibility and scalability of robot control systems by design.
This PhD thesis aims at finding a global robot navigation strategy for rugged off-road terrain which is robust against inaccurate self-localization, scalable to large environments, but also cost-efficient, e.g. able to generate navigation paths which optimize a cost measure closely related to terrain traversability. In order to meet this goal, aspects of both metrical and topological navigation techniques are combined. A primarily topological map is extended with the previously lacking capability of cost-efficient path planning and map extension. Further innovations include a multi-dimensional cost measure for topological edges, a method to learn these costs based on live feedback from the robot and a set of extrapolation methods to predict the traversability costs for untraversed edges. The thesis presents two sophisticated new image analysis techniques to optimize cost prediction based on the shape and appearance of surrounding terrain. Experimental results indicate that the proposed global navigation system is indeed able to perform cost-efficient, large scale path planning. At the same time, the need to maintain a fine-grained, global world model which would reduce the scalability of the approach is avoided.
Zur Zeit haben Industrieroboter nur eine sehr begrenzte Wahrnehmung ihrer Umwelt. Wenn sich Menschen im Arbeitsraum des Roboters aufhalten sind sie daher gefährdet. Durch eine Einteilung der möglichen Roboterbewegung in verschiedene Klassen kann gezeigt werden, dass die für einen Menschen im Arbeitsraum gefährlichste Bewegung die freie Transferbewegung ist. Daher besteht die betrachtete Aufgabe darin, diese Transferbewegung eines Manipulators durchzuführen, ohne mit dynamischen Hindernissen, wie zum Beispiel Menschen, zu kollidieren. Das vorgestellte SIMERO-System realisiert eine globale Ganzarmkollisionsvermeidung auf der Basis von Bildern stationärer Kameras. Das System gliedert sich in die vier Hauptkomponenten Bildverarbeitung, Robotermodellierung, Kollisionserkennung und Bahnplanung. Diese Komponenten werden im einzelnen vorgestellt.