Browsing by Subject "Roboter"

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Artificial intelligence and robots in services : theory and management of (future) human–robot service interactions
(2023) Blaurock, Marah Karin; Büttgen, Marion
During the past decade, service robots have increasingly been deployed in a wide variety of services, where they co-produce service outcomes with and for the benefit of internal or external customers within human–robot service interactions (HRSI). Although the introduction of different service robot types into the marketplace promises efficiency gains, it changes premises of service encounter theory and practice fundamentally. Moreover, introducing service robots without considering external or internal customers’ needs can lead to negative service outcomes. This thesis aims to generate knowledge on how the introduction of different service robot types (i.e., embodied and digital service robots) in internal and external service encounters changes fundamental premises of service encounter theory and impacts HRSI outcomes. In doing so, it leverages different scientific methods and focuses on external service encounters with digital and embodied service robots, as well as internal service encounters with digital service robots. Chapter 2 aims to advance service encounter theory in the context of HRSI in external service encounters by conceptually developing a service encounter theory evaluation scheme to assess a theory’s fit to explain HRSI-related phenomena. The scheme includes individual and contextual factors that bound theoretical premises and, hence, supports scholars in assessing standing service encounter theories. The chapter also puts forth an exemplary assessment of role theory and provides detailed avenues for future research. Chapter 3 aims to synthesize the great wealth of knowledge on HRSI related to external service encounters with embodied service robots. By conducting a comprehensive systematic literature review, the chapter identifies 199 empirical research articles across scientific fields that can inform service research on how to successfully introduce service robots into the organizational frontline. To organize the plethora of research findings, this chapter develops a new structuring framework (D3: design, delegate, deploy). It utilizes this framework to provide a comprehensive overview of the empirical HRSI literature, delineates practical implications, and identifies gaps in literature to identify promising future research avenues. Chapter 4 also addresses HRSI in external service encounters but focuses specifically on the transformative potential of embodied service robots to enhance vulnerable consumers’ (i.e., children and older adults) well-being in social isolation. To identify how different robots can enhance well-being, this chapter follows a conceptual approach and integrates findings from service research, social robotics, social psychology, and medicine. The chapter develops a typology of robotic transformative service (i.e., entertainer, social enabler, mentor, and friend) as a function of consumers state of social isolation, well-being focus, and robot capabilities and a future research agenda for robotic transformative service research (RTSR). This work guides service consumers and providers, as well as robot developers, in identifying and developing the most appropriate robot type for advancing the well-being of vulnerable consumers in social isolation. Finally, Chapter 5 focuses on HRSI research in the context of interactions with digital service robots in internal service encounters. Based on a comprehensive literature review paired with a qualitative study, it conceptionally develops a new concept of a collaborative, digital service robot: a collaborative intelligence system (i.e., CI system) that co-produces service with employees. Drawing from service encounter needs theory, the chapter also empirically tests the effect of CI systems on employee need fulfillment (i.e., need for control, cognition, self-efficacy, and justice) and, in turn, on responsibility taking in two scenario-based experiments. The results uncover divergent mechanisms of how the fulfillment of service encounter needs drives the effect of CI systems on outcome responsibility for different employee groups. Service scholars and managers benefit from a blueprint for designing collaborative digital service robots and an understanding of their effects on employee outcomes in service co-production. In summary, this thesis contributes to literature by providing new insights into different types of HRSI by consolidating HRSI knowledge, developing and advancing HRSI concepts and theory, and empirically investigating HRSI-related phenomena. The new insights put forth in this thesis are discussed and implications for service theory and practice are delineated.
Automation, robots and wage inequality in Germany : a decomposition analysis
(2020) Schmid, Ramona; Brall, Franziska
We analyze how and through which channels wage inequality is affected by the rise in automation and robotization in the manufacturing sector in Germany from 1996 to 2017. Combining rich linked employer-employee data accounting for a variety of different individual, firm and industry characteristics with data on industrial robots and automation probabilities of occupations, we are able to disentangle different potential causes behind changes in wage inequality in Germany. We apply the recentered influence function (RIF) regression based Oaxaca-Blinder (OB) decomposition on several inequality indices and find evidence that besides personal characteristics like age and education the rise in automation and robotization contributes significantly to wage inequality in Germany. Structural shifts in the workforce composition towards occupations with lower or medium automation threat lead to higher wage inequality, which is observable over the whole considered time period. The effect of automation on the wage structure results in higher inequality in the 1990s and 2000s, while it has a significant decreasing inequality effect for the upper part of the wage distribution in the more recent time period.
Automatisierung, Wachstum und Ungleichheit
(2018) Schwarzer, Johannes; Prettner, Klaus; Geiger, Niels
Die Automatisierung stellt eines der wichtigsten Phänomene dar, welche aktuell innerhalb der Wirtschaftswissenschaften und der breiteren Öffentlichkeit diskutiert werden. Dabei finden sich in Bezug auf die Frage, wie sich die Automatisierung gesamtwirtschaftlich auswirkt, sehr unterschiedliche Positionen: Am einen Ende wird auf die negativen Beschäftigungseffekte verwiesen, wenn Menschen mehr und mehr durch Maschinen ersetzt werden und ihre am Markt angebotene Arbeitsleistung nicht mehr nachgefragt somit obsolet wird. Gleichzeitig wird die Automatisierung auch für einen Anstieg der wirtschaftlichen Ungleichheit verantwortlich gemacht. Optimistischere Stimmen verweisen andererseits auf die Entwicklung seit der Industriellen Revolution, die durch fortlaufende technologische Veränderungen mit hohem Produktivitätswachstum und damit starken Wohlfahrtssteigerungen einherging, ohne dass es langfristig zu Massenarbeitslosigkeit gekommen ist. Der vorliegende Aufsatz diskutiert einige allgemein relevante empirische Daten und skizziert ein einfaches theoretisches Wachstumsmodell zur Analyse der Automatisierung. Die hierbei festgehaltenen Ergebnisse werden unter Bezugnahme auf die aktuelle wirtschaftswissenschaftliche Literatur zu den bisherigen und für die Zukunft zu erwartenden ökonomischen Effekten der Automatisierung vertieft und erweitert. Aus den verschiedenen Ansatzpunkten und Überlegungen werden schließlich wirtschaftspolitische Handlungsmöglichkeiten abgeleitet, wobei auch jeweils diskutiert wird, welchen Einschränkungen diese Maßnahmen unterliegen.
Perception for context awareness of agricultural robots
(2018) Reiser, David; Griepentrog, Hans
Context awareness is one key point for the realisation of robust autonomous systems in unstructured environments like agriculture. Robots need a precise description of their environment so that tasks could be planned and executed correctly. When using a robot system in a controlled, not changing environment, the programmer maybe could model all possible circumstances to get the system reliable. However, the situation gets more complex when the environment and the objects are changing their shape, position or behaviour. Perception for context awareness in agriculture means to detect and classify objects of interest in the environment correctly and react to them. The aim of this cumulative dissertation was to apply different strategies to increase context awareness with perception in mobile robots in agriculture. The objectives of this thesis were to address five aspects of environment perception: (I) test static local sensor communication with a mobile vehicle, (II) detect unstructured objects in a controlled environment, (III) describe the influence of growth stage to algorithm outcomes, (IV) use the gained sensor information to detect single plants and (V) improve the robustness of algorithms under noisy conditions. First, the communication between a static Wireless Sensor Network and a mobile robot was investigated. The wireless sensor nodes were able to send local data from sensors attached to the systems. The sensors were placed in a vineyard and the robot followed automatically the row structure to receive the data. It was possible to localize the single nodes just with the exact robot position and the attenuation model of the received signal strength with triangulation. The precision was 0.6 m and more precise than a provided differential global navigation satellite system signal. The second research area focused on the detection of unstructured objects in point clouds. Therefore, a low-cost sonar sensor was attached to a 3D-frame with millimetre level accuracy to exactly localize the sensor position. With the sensor position and the sensor reading, a 3D point cloud was created. In the workspace, 10 individual plant species were placed. They could be detected automatically with an accuracy of 2.7 cm. An attached valve was able to spray these specific plant positions, which resulted in a liquid saving of 72%, compared to a conventional spraying method, covering the whole crop row area. As plants are dynamic objects, the third objective of describing the plant growth with adequate sensor data, was important to characterise the unstructured agriculture domain. For revering and testing algorithms to the same data, maize rows were planted in a greenhouse. The exact positions of all plants were measured with a total station. Then a robot vehicle was guided through the crop rows and the data of attached sensors were recorded. With the help of the total station, it was possible to track down the vehicle position and to refer all data to the same coordinate frame. The data recording was performed over 7 times over a period of 6 weeks. This created datasets could afterwards be used to assess different algorithms and to test them against different growth changes of the plants. It could be shown that a basic RANSAC line following algorithm could not perform correctly under all growth stages without additional filtering. The fourth paper used this created datasets to search for single plants with a sensor normally used for obstacle avoidance. One tilted laser scanner was used with the exact robot position to create 3D point clouds, where two different methods for single plant detection were applied. Both methods used the spacing to detect single plants. The second method used the fixed plant spacing and row beginning, to resolve the plant positions iteratively. The first method reached detection rates of 73.7% and a root mean square error of 3.6 cm. The iterative second method reached a detection rate of 100% with an accuracy of 2.6 - 3.0 cm. For assessing the robustness of the plant detection, an algorithm was used to detect the plant positions in six different growth stages of the given datasets. A graph-cut based algorithm was used, what improved the results for single plant detection. As the algorithm was not sensitive against overlaying and noisy point clouds, a detection rate of 100% was realised, with an accuracy for the estimated height of the plants with 1.55 cm. The stem position was resolved with an accuracy of 2.05 cm. This thesis showed up different methods of perception for context awareness, which could help to improve the robustness of robots in agriculture. When the objects in the environment are known, it could be possible to react and interact smarter with the environment as it is the case in agricultural robotics. Especially the detection of single plants before the robot reaches them could help to improve the navigation and interaction of agricultural robots.