This paper investigates anthropomorphism of a robot arm during a cooperative human-robot pick-and-place task, while varying cognitive load of test persons. Test persons are required to repeatedly provide a Lego brick for the robot by alternatingly putting it onto one of two trays. The robot then picks it up and puts it in front of the test person again. Cognitive load is varied by whether or not an initially given 8-digit number has to be remembered by the test person. Dimensions of anthropomorphism are acquired using the HRIES questionnaire and cognitive load is acquired using two state-of-the-art questionnaires. Results show a significant correlation of perceived sociability and animacy on mental demand and cognitive load, but only in the high load condition. It is suggested, that cognitive load should be considered during cooperative task design because resulting variations of anthropomorphism may impact cooperative task performance.

Abstract

This paper presents results from a study on the impact of negative attitudes towards robots on pedestrians’ needs for technological communication capabilities of autonomous vehicles and preferred communication modalities. Further, the amount of prior information on autonomous vehicles given to test persons is varied. The study is realized in terms of an imagination scenario. Results show a significant dependency of the demand for communication of autonomous vehicles with pedestrians on the extent of negative attitudes towards robots as well as a general demand for such communication capabilities. Interestingly, these findings are independent of the amount of prior information. Differences of preferred communication modalities with respect to negative attitudes or prior information are not found. The results of this study emphasize the importance of vehicle-pedestrian communication, particularly, using multi-modal interfaces in future autonomous driving technology.

Dieser Beitrag präsentiert die Ergebnisse einer Studie über die Auswirkungen negativer Einstellungen gegenüber Robotern auf die Bedürfnisse von Fußgängern nach technologischen Kommunikationsmöglichkeiten autonomer Fahrzeuge und bevorzugte Kommunikationsmodalitäten. Darüber hinaus wird die Menge an Vorinformationen zu autonomen Fahrzeugen, die den Testpersonen gegeben werden, variiert. Die Studie wird anhand eines imaginären Szenarios durchgeführt. Die Ergebnisse zeigen eine signifikante Abhängigkeit des Bedarfs an Kommunikation autonomer Fahrzeuge mit Fußgängern vom Ausmaß negativer Einstellungen gegenüber Robotern sowie eines Bedarfs an solchen Kommunikationsfähigkeiten allgemein. Interessanterweise sind diese Erkenntnisse unabhängig von der Menge an Vorinformationen. Unterschiede der bevorzugten Kommunikationsmodalitäten in Bezug auf negative Einstellungen oder Vorinformationen wurden nicht festgestellt. Die Ergebnisse dieser Studie unterstreichen die Bedeutung der Fahrzeug-Fußgänger-Kommunikation, insbesondere unter Verwendung multimodaler Schnittstellen in zukünftigen autonomen Fahrtechnologien.

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Laser sintering, also known as powder bed fusion—laser beam, employs a laser to sinter-powdered polymeric materials, such as Polyamide 12. Despite the process, a significant portion of the powder remains unsintered. However, due to elevated temperatures, material degradation occurs, altering its chemical characteristics. Industrially, a common practice involves utilizing a mixture of virgin and aged powder, with the latter having undergone multiple thermal cycles without sintering. Developing an effective powder recycling methodology is fundamental for fully realizing the potential of Polyamide 12. This work focuses the attention on reused powder to manufacture parts in Polyamide 12 through selective laser sintering and evaluates the correlation between the properties of reused powder and the strength of manufactured part through six consecutive printing processes. To achieve this, a wide experimental approach was planned and developed to arrive to establish a clear relationship between the degree of crystallinity of the powder and the ultimate tensile strength of the part made from that powder. This relationship may be used to choose the kind of powder based on the strength required to the manufactured part in exercise. At the same time, if only a kind of powder is available, this relationship allows to predict the mechanical strength of the part manufactured.

Artificial intelligence (AI) has become an integral part of business and is being used more and more frequently due to its versatile capabilities. The use of AI can be a decisive competitive advantage, especially for customer-centric and innovation-orientated business models. Traditionally, the innovation process has primarily been driven by marketing experts, engineers, and designers. At the same time, however, there is growing interest in the integration of generative AI in the innovation process. More and more companies are integrating conversational language models (LLMs) such as ChatGPT as problem-solving agents in their innovation process to effectively meet the growing demands of customers. With the growing interest of companies, research interest in the interface between AI and innovation is also growing. However, the field of research to date is still fragmented and study results are ori-entated as individual case studies in individual fields. To date, there has been a lack of a bundled approach that integrates and systematizes current studies. This article analyses how AI can be integrated into the phases of the innovation process. A systematic literature review sheds light on its potential and challenges within the innovation process. The results indicate that AI can be used in all phases of the innovation process, taking on the role of a supporter, an extension or an independent actor. Accordingly, AI can contribute to the reorganization of innovation processes in companies in different ways. Both opportunities and challenges for companies become apparent.