The automation of production processes with large process variability and a low batch size can be very difficult and non-economic. Using the example of manufacturing carbonfibre-reinforced polymers (CFRP) which represents a complex, currently hardly automated process, we present a backwardoriented approach for offline programming of complex manufacturing tasks. We focus on an automatic process definition which is supported by expert knowledge where required. Due to domain specific software modules, user interaction is intuitive and tailored to CFRP experts. This leads to significant time-savings compared to currently used teach-in approaches. Moreover, we introduce an extensible offline programming platform which is able to meet the high requirements of CFRP manufacturing.

Tasks that change the physical state of a robot take a considerable amount of time to execute. However, many robot applications spend the execution time waiting, although the following tasks might require time to prepare. This paper proposes to amend tasks with a description of their expected outcomes, which allows planning successive tasks based on this information. The suggested approach allows sequential and parallel composition of tasks, as well as reactive behavior modeled as state machines. The paper describes the means of modeling and executing these tasks, details different possibilities of planning in state machine tasks, and evaluates the benefits achievable using the approach.

No abstract

Considering initiatives like Industry 4.0 or the Industrial Internet of Things, robots will play an important role in intelligent factories, producing highly customized products with high variability and in small lot sizes. In this setting, complexity of planning and programming such robotic applications grows due to the drastic increase in flexibility, performance and robustness required. In this paper, we propose a tool-supported methodology for the development of control software for dynamically forming multi-functional robot teams. The main challenges for achieving this overall goal are modeling of robot team skills, techniques for automatically deriving process steps from the products' construction plans, finding allocations of those steps to possible robot teams with compatible skills and calculating collision-free execution schedules with a high degree of parallelization to improve cycle times. The proposed approach integrates process experts and automation experts on all levels. Two case studies will serve as test beds to the developed approach: production of carbon-fiber reinforced polymers and assembly of furniture.

When automating small-batch manufacturing processes, the time spent for process planning and robot programming becomes more important. This paper proposes an automated process including construction plan analysis, process planning and execution to reduce the amount of manual work required. The process starts by analyzing the structure of the desired product and deriving required process step results, then uses heuristic search to find possible production steps and task assignments, and concludes by simulating or executing the resulting production plan. The approach is evaluated on a case study with a simulated robot automatically building different LEGO R DUPLO R structures starting from a 3D model defining the desired product.