Robotics

The paper analyzes older people’s expectations and perceptions about welfare technology and in particular about assistant robots in elderly care. Assistant robots may extend autonomy in old age and provide support for caregivers. In this study attitudes of older people, caregivers and care managers were collected through focus group discussions, by exploring seven categories that need to be addressed to improve orientation towards assistant robot technology in care. Therefore, an adequate dissemination of information may enhance people’s acceptance and reduces fear toward technology in care.

The phase-out of nuclear energy decided by the politicians and the goal of significantly aligning the energy mix with renewable energies will give the industry great growth potential. Digitalization and the resulting technologies, such as sensors, robotics and assistance systems, artificial intelligence, virtual reality and augmented reality, are helping companies realise their potential. The study “Industrialization of the Wind Industry” by the Technical University of Munich has shown that digitalization will have a positive effect on the “Levelized Cost of Energy” (LCOE).

Software development of industrial robots requires interdisciplinary knowledge and technical experience. Due to the heterogeneity of the manufacturer-dependent programming languages and tools, robot programming remains highly complex, although robots themselves are flexible and can be used for a wide range of applications. To support different roles during the development, including component provider, application developer, system integrator and end user, a model based approach was developed in the research project ReApp. The data exchange format AutomationML was used for the modelling of robot components and systems. Based on domain ontologies, the AutomationML models were processed semantically and converted to a machine-interpretable information model, from which source code was generated.

The methods presented allow the splitting of classic monolithic numerical controls of industrial robots and machine tools into their functional units. The core functionalities can then be brought onto different computers in even separate places. Using techniques of augmented reality allows enriching a captured scene with additional information, as a virtual model of the industrial robot or the planned paths. Combining these approaches leads to a simplified programming task for industrial robots as the programs can be visualized in their context. This decreases setup time and improves quality.

Intelligent and autonomous robots are an essential part of the development of industry 4.0 solutions. They will act as a direct interaction partner to humans together in teams and perform works that are much more complex than today‘s typical tasks for industrial robots. Such robots need to deal with a confusing and unpredictable environment and have to react to unforeseeable events. In order to capture this environment and to plan actions, real-time processing of complex sensor information is necessary. Conventional computer architectures appear to be insufficient for such kind of tasks. To solve this problem, hardware accelerators for robotics based on the dataflow paradigm are developed at the Robotics Innovation Center of the German Research Center for Artificial Intelligence.

In contrast to the use of robots in standardized production processes, robots must be flexible and adaptable within dynamic logistics processes in order to cope with variable environmental conditions and non-standardized goods. Due to the recent advances in the field of artificial intelligence and networking through industry 4.0, robots will perform complex tasks in logistics in a reliable way in future. A crucial component of a robot system represents the interpretation of the work environment with the help of multi-modal sensor systems, especially image processing systems. This paper describes applications for robotic systems in logistics as well as a concrete example of focusing on the interpretation of multi-modal sensor data for the automation of a logistics task.

New areas of application arise for robots due to collaborative robots and new operation concepts. We describe important characteristics of these robotic systems and, as a possible application area for these systems, we present in this article a robotic assistant system for manual manufacturing tasks, which is controlled with a multi-modal user interface.

In many fields of production for processing tasks with high frequency and repeatability, robots are commonly used. However, the use of industrial and service robots also increases in the logistics sector. Container handling, particularly the unloading of for example coffee sacks, has enormous potential for automation with robotics. The heavy weight together with the deformability of these sacks and unpredictable stack situations inside a container have the highest mechanical and cognitive demands on the gripper technology and the corresponding gripping strategy. This paper describes the development and implementation of such a gripping process.

Due to the growing application of robotic systems in logistic companies, the demand of qualified professionals is growing. The potentials of individualized robotic solutions are currently not exploited by small and medium enterprises. Therefore a target group specific further education for robotics in logistics has been developed. The modular design ensures the close connection of the education modules to the core work processes of the industry sector. The “Lessons Learned” summarize important insights from the educational scientific examinations in logistic companies as well as from the execution of the education modules.