A reliable, accurate, and yet simple dynamic model is important to analyzing, designing, and controlling hybrid rigid–continuum robots. Such models should be fast, as simple as possible, and user-friendly to be widely accepted by the ever-growing robotics research community. In this study, we introduce two new modeling methods for continuum manipulators: a general reduced-order model (ROM) and a discretized model with absolute states and Euler–Bernoulli beam segments (EBA). In addition, a new formulation is presented for a recently introduced discretized model based on Euler–Bernoulli beam segments and relative states (EBR). We implement these models in a Matlab software package, named TMTDyn, to develop a modeling tool for hybrid rigid–continuum systems. The package features a new high-level language (HLL) text-based interface, a CAD-file import module, automatic formation of the system equation of motion (EOM) for different modeling and control tasks, implementing Matlab C-mex functionality for improved performance, and modules for static and linear modal analysis of a hybrid system. The underlying theory and software package are validated for modeling experimental results for (i) dynamics of a continuum appendage, and (ii) general deformation of a fabric sleeve worn by a rigid link pendulum. A comparison shows higher simulation accuracy (8–14% normalized error) and numerical robustness of the ROM model for a system with a small number of states, and computational efficiency of the EBA model with near real-time performances that makes it suitable for large systems. The challenges and necessary modules to further automate the design and analysis of hybrid systems with a large number of states are briefly discussed.
Abstract. So far, ontologies in the Semantic Web and models in model-driven engineering have been developed mainly in isolation. It seems that due to a lack of communication between communities, modelling concepts have been designed similarly in both paradigms without ensuring their orthogonality. On the long run, this will replicate efforts and cannot be afforded by either community. Hence, this chapter discusses the role of ontologies, models, and meta-models in the model-driven engineering (MDE). To show how ontologies can be employed in MDE, in particular, in its variant model-driven architecture (MDA), the chapter presents a meta-modelling hierarchy that is aware of ontologies-that is, an ontology-aware mega-model of MDE. Based on the insight of [38] that the main difference of models and ontologies lies in their descriptiveness resp. prescriptiveness, the role of ontologies in this meta-pyramid is to describe the existing world, the environment, and the domain of the system (analysis), while the role of system models is to specify and control the system under study itself on various levels of abstraction (design and implementation). Consequently, in this scheme, MDE starts from ontologies, refines, and augments them towards system models, respecting their relationships to prescriptive models on all metalevels.
Abstract. Since the seminal book by the Gang of Four, design patterns have proven an important tool in software development. Over time, more and more patterns have been discovered and developed. The sheer amount of patterns available makes it hard to find patterns useful for solving a specific design problem. Hence, tools supporting searching and finding design patterns appropriate to a certain problem are required. To develop such tooling, design patterns must be described formally such that they can be queryed by the problem to be solved. Current approaches to formalising design patterns focus on the solution structure of the pattern rather than on the problems solved. In this paper, we present a formalisation of the intent of the 23 patterns from the Gang-of-Four book. Based on this formalisation we have developed a Design Pattern Wizard that proposes applicable design patterns based on a description of a design problem.
Abstr act. Managing variability is a challenging issue in software-product-line engineering. A key part of variability management is the ability to express explicitly the relationship between variability models (expressing the variability in the problem space, for example using feature models) and other artefacts of the product line, for example, requirements models and architecture models. Once these relations have been made explicit, they can be used for a number of purposes, most importantly for product derivation, but also for the generation of trace links or for checking the consistency of a product-line architecture. This paper bootstraps techniques from product-line engineering to produce a family of languages for variability management for easing the creation of new members of the family of languages. We show that developing such language families is feasible and demonstrate the flexibility of our language family by applying it to the development of two variability-management languages.
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