UAV inspection of large components: indoor navigation relative to structures

The inspection of large structures is increasingly carried out with the help of Unmanned Aerial Vehicles (UAVs). When navigating relative to the structure, multiple data sources can be used to determine the position of the UAV. Examples include track data from an installed camera and sensor data from the orientation sensors of the UAV. This paper deals with the fusion of this data and its use for navigation alongside the structure. For the sensor fusion, a concept is developed using a Kalman filter and evaluated simulatively in a prototype. The calculated position data are also fed into a vector flight control system, which dynamically calculates and flies a trajectory along the component using the potential field method. This is done taking into account obstacles detected by the onboard sensors of the UAV. The established concept is then implemented with the Robot Operating System (ROS) and evaluated simulatively

Towards models of conceptual and procedural operator knowledge

To increase the utility of semantic industrial information models we propose a methodology to incorporate extracted operator knowledge, which we assume to be present in the form of rules, in knowledge graphs. To this end, we present multiple modelling patterns that can be combined depending on the required complexity. Aiming to combine information models with learning systems we contemplate desired behaviours of embeddings from a predictive quality perspective and provide a suited embedding methodology. This methodology is evaluated on a real world dataset of a fused deposition modelling process

Towards fully automated inspection of large components with UAVs: offline path planning

Automation mechanisms are increasingly established in the field of visual inspections. UAVs can be used for particularly large components, such as those used in ship production and for critical infrastructures. This paper concentrates on the problem of visual inspection in the field of perspective-dependent route planning. It is shown how the requirements for such a system can be implemented and elaborated. Furthermore we investigate how sensor positions can be calculated offline, based on optical and geometrical requirements and how a trajectory can be planned which contains the found sensor positions for each given area on the component. It is shown how the systems architecture can be designed in order to be able to adapt it to different requirements for the planning of sensor positions and trajectory. The implementation was tested in a simulation environment, evaluated using a benchmark data set and it was shown how above-average results can be achieved on this data set

Towards a real-time capable plug & produce environment for adaptable factories

Industrial manufacturing is currently undergoing a transformation from mass production with inflexible production systems to individual production with adaptable cells. In order to ensure this adaptability of these systems, technologies such as plug & produce are needed, to integrate, modify and remove devices at runtime. Therefor an exact description of the system, the products and the capabilities / skills of the devices is essential as well as a network for communication between the devices. Deterministic data transmission is particularly important for distributed control systems. We propose an architecture for plug & produce mechanisms with hard real-time capable communication paths between the cyber-physical components using OPC UA PubSub over TSN and the ability to load and execute real-time critical tasks at runtime

RealCaPP: Real-time capable Plug & Produce communication platform with OPC UA over TSN for distributed industrial robot control

The industry of tomorrow is changing from central hierarchical industrial and robot controls to distributed controls on the industrial shop floor. These fundamental changes in network structure make it possible to implement technologies such as Plug & Produce. In other words, to integrate, change and remove devices without much effort at runtime. In order to achieve this goal, a uniform architecture with defined interfaces is necessary to establish real-time communication between the varying devices. Therefore, we propose an approach to use the combination of OPC UA and TSN to automatically configure real-time capable communication paths between robots and other cyber-physical components and execute real-time critical tasks in the distributed control system

ROSSi a graphical programming interface for ROS 2

The Robot Operating System (ROS) offers developers a large number of ready-made packages for developing robot programs. The multitude of packages and the different interfaces or adapters is also the reason why ROS projects often tend to become confusing. Concepts of model-driven software development using a domain-specific modeling language could counteract this and at the same time speed up the development process of such projects. This is investigated in this paper by transferring the core concepts from ROS 2 into a graphical programming interface. Elements of established graphical programming tools are compared and approaches from modeling languages such as UML are used to create a novel approach for graphical development of ROS projects. The resulting interface is evaluated through the development of a project built on ROS, and the approach shows promise towards facilitating work with the Robot Operating System