The development of software applications for robotics is a challenging task for engineers due to the inherent parallelism of the code and the multitude of flexible factors that must be considered. Robotic assembly, a specific type of robotic application, involves the construction of pre-determined products using designated pieces. This further complicates the engineering task, as not only the robots must be considered but also the construction materials and the product itself. While several specialized solutions for robotic assembly exist, they are typically handcrafted and are tightly bound to the robots and materials they were designed for. Thus, it is hard for these solutions to be extended to respond to changes and newly added modules. This work proposes a model-driven approach to robotic assembly supporting diverse domains that can accommodate the seamless integration of robots. Relational reference attribute grammars are used for both metamodels and runtime models. To assess the feasibility and effectiveness of our proposed approach, we conducted a case study based on an industrial research project.

Even though model-driven engineering reduces complexity during the development of self-adaptive systems and [email protected] enables using them during runtime, connecting models to different external systems still involves manual work. Those connections are essential to the complete system, as they enable external systems to react to changes in the internal model and vice versa. In our case, the model is based on Relational Reference Attribute Grammars, an extension of Attribute Grammars to enable conceptual models at runtime while retaining their benefits of modular specification and an incremental evaluation scheme. We present an approach to enable concise specification of the causal connection and needed transformations to match required formats or semantics. To show its applicability, a case study showing the coordination of multiple industrial robot arms using models is presented. We show that using our approach, connections can be specified more concisely while maintaining the same efficiency as hand-written code. The artefact comprising all source code and an executable version of the case studies is available at https://doi.org/10.5281/zenodo.7009758.

Abstract To cope with the increased complexity of systems, models are used to capture what is considered the essence of a system. Such models are typically represented as a graph, which is queried to gain insight into the modelled system. Often, the results of these queries need to be adjusted according to updated requirements and are therefore a subject of maintenance activities. It is thus necessary to support writing model queries with adequate languages. However, in order to stay meaningful, the analysis results need to be refreshed as soon as the underlying models change. Therefore, a good execution speed is mandatory in order to cope with frequent model changes. In this paper, we propose a benchmark to assess model query technologies in the presence of model change sequences in the domain of social media. We present solutions to this benchmark in a variety of 11 different tools and compare them with respect to explicitness of incrementalization, asymptotic complexity and performance.

Just like current software systems, models are characterised by increasing complexity and rate of change. Yet, these models only become useful if they can be continuously evaluated and validated. To achieve sufficiently low response times for large models, incremental analysis is required. Reference Attribute Grammars (RAGs) offer mechanisms to perform an incremental analysis efficiently using dynamic dependency tracking. However, not all features used in conceptual modelling are directly available in RAGs. In particular, support for non-containment model relations is only available through manual implementation. We present an approach to directly model uni- and bidirectional non-containment relations in RAGs and provide efficient means for navigating and editing them. This approach is evaluated using a scalable benchmark for incremental model editing and the JastAdd RAG system. Our work demonstrates the suitability of RAGs for validating complex and continuously changing models of current software systems.

We experimentally investigate the mutual influence of application- and platform-level adaptations in a virtualized cluster environment. At the application level, applications can adapt to a changing execution environment by dynamically exchanging components that enable them to trade energy for utility and vice versa. Likewise, at the platform level, virtual machine monitors can migrate virtual machines from one server to another either to consolidate workloads and switch-off underutilized servers or to distribute the workload of overloaded servers. Our experiment quantify impacts of various types of adaptations on QoS, power consumption, and energy-overhead.

In this article we present a method to estimate the pose and orientation of a planar pattern observed by a calibrated camera. The method takes advantage of the re-projective part of the camera mapping and presents a closed form solution assuming three equidistant collinear points. Only one image is used for the pose estimation. If we know the distances between these point we can reconstruct the 3D position of the points completely. If the exact distance is unknown the reconstruction is correct up to a scale factor. The proposed algorithm has no iterations, no hidden non-linear parts, and uses no trigonometric functions.