Industrial robot ethics: facing the challenges of human-robot collaboration in future manufacturing systems

As a result of significant advances in information and communications technology the manufacturing industry is facing revolutionary changes whereby production processes will become increasingly digitised and interconnected cyber-physical systems. A key component of these new complex systems will be intelligent automation and human-robot collaboration. Industrial robots have traditionally been segregated from people in manufacturing systems because of the dangers posed by their operational speeds and heavy payloads. However, advances in technology mean that we will soon see large-scale robots being deployed to work more closely and collaboratively with people in monitored manufacturing sytems and widespread introduction of small-scale robots and assistive robotic devices. This will not only transform the way people are expected to work and interact with automation but will also involve much more data provision and capture for performance monitoring. This paper discusses the background to these developments and the anticipated ethical issues that we now face as people and robots become able to work collaboratively in industry

3D Assembly Group Analysis for Cognitive Automation

A concept that allows the cognitive automation of robotic assembly processes is introduced. An assembly cell comprised of two robots was designed to verify the concept. For the purpose of validation a customer-defined part group consisting of Hubelino bricks is assembled. One of the key aspects for this process is the verification of the assembly group. Hence a software component was designed that utilizes the Microsoft Kinect to perceive both depth and color data in the assembly area. This information is used to determine the current state of the assembly group and is compared to a CAD model for validation purposes. In order to efficiently resolve erroneous situations, the results are interactively accessible to a human expert. The implications for an industrial application are demonstrated by transferring the developed concepts to an assembly scenario for switch-cabinet systems

Robotic learning of force-based industrial manipulation tasks

Even with the rapid technological advancements, robots are still not the most comfortable machines to work with. Firstly, due to the separation of the robot and human workspace which imposes an additional financial burden. Secondly, due to the significant re-programming cost in case of changing products, especially in Small and Medium-sized Enterprises (SMEs). Therefore, there is a significant need to reduce the programming efforts required to enable robots to perform various tasks while sharing the same space with a human operator. Hence, the robot must be equipped with a cognitive and perceptual capabilities that facilitate human-robot interaction. Humans use their various senses to perform tasks such as vision, smell and taste. One sensethat plays a significant role in human activity is ’touch’ or ’force’. For example, holding a cup of tea, or making fine adjustments while inserting a key requires haptic information to achieve the task successfully. In all these examples, force and torque data are crucial for the successful completion of the activity. Also, this information implicitly conveys data about contact force, object stiffness, and many others. Hence, a deep understanding of the execution of such events can bridge the gap between humans and robots. This thesis is being directed to equip an industrial robot with the ability to deal with force perceptions and then learn force-based tasks using Learning from Demonstration (LfD).To learn force-based tasks using LfD, it is essential to extract task-relevant features from the force information. Then, knowledge must be extracted and encoded form the task-relevant features. Hence, the captured skills can be reproduced in a new scenario. In this thesis, these elements of LfD were achieved using different approaches based on the demonstrated task. In this thesis, four robotics problems were addressed using LfD framework. The first challenge was to filter out robots’ internal forces (irrelevant signals) using data-driven approach. The second robotics challenge was the recognition of the Contact State (CS) during assembly tasks. To tackle this challenge, a symbolic based approach was proposed, in which a force/torque signals; during demonstrated assembly, the task was encoded as a sequence of symbols. The third challenge was to learn a human-robot co-manipulation task based on LfD. In this case, an ensemble machine learning approach was proposed to capture such a skill. The last challenge in this thesis, was to learn an assembly task by demonstration with the presents of parts geometrical variation. Hence, a new learning approach based on Artificial Potential Field (APF) to learn a Peg-in-Hole (PiH) assembly task which includes no-contact and contact phases. To sum up, this thesis focuses on the use of data-driven approaches to learning force based task in an industrial context. Hence, different machine learning approaches were implemented, developed and evaluated in different scenarios. Then, the performance of these approaches was compared with mathematical modelling based approaches.</div

Model-based operator guidance in interactive, semi-automated production processes

This contribution focuses on the task of guiding and supervision of technical processes realized by human operators. The review of publications of the last decades discloses that especially technical processes with strong interconnection of human operator and manufacturing process are not adequately addressed by the evolved automation approaches. Integrating human process knowledge and experience into the resulting automation system is still a major concern. Besides the introduction of automation in a handcrafting process that is increasing the overall system complexity, the design of the human-machine interface to the automation system is of central importance. Within this thesis, the trade-off between manual manufacturing and automation is addressed by a semi-automation approach. The application example is the no-bake molding process, a mold manufacturing process for casts that is traditionally handmade. Within this process the human operator plays a central role (i.e. knowledge and expertise), whereas the (intelligent) automation is carrying out physical operation, which is guided and supervised by the human operator. This is achieved by experimentally identified quality representing process variables that allow for in-process feedback to the human operator. Process guiding assistance is given using a formalization approach of the human-automation-interaction. By deducing situative information of interest from the resulting human-automation-system model with respect to the current process goal, the established process model is used for supervision and assistance of the overall process. The design of the human-machine-interface is based on a detailed analysis of the handcrafted process and is realized as a direct, intuitively usable, marker-based interaction technique. The integrated human-automation-system and the corresponding human-machine-interface with process guidance assistance functionality is initially evaluated. The results are discussed for the future work with respect to the individual, human operator-specific process understanding and process reproducibility.Diese Arbeit befasst sich mit Fachkraftaufgaben in der Führung und Überwachung von technischen Prozessen. Die Übersicht der Publikationen der letzten Jahrzehnte eröffnet, dass insbesondere technische Prozesse mit enger Verknüpfung von Mensch und Herstellungsprozess bei den entwickelten Automatisierungsansätzen nicht hinreichend berücksichtigt werden. Die Integration von Prozesswissen und -erfahrung in das resultierende Automatisierungssystem bleibt eine offene Fragestellung. Neben der Einführung von Automation in Handarbeitsprozesse, die die Komplexität des Gesamtsystems erhöhen, ist die Gestaltung der Mensch-Maschine-Schnittstelle zum Automatisierungssystem von zentraler Bedeutung. Der Konflikt zwischen Handarbeit und Automatisierung wird in dieser Arbeit durch die Einführung einer Teilautomatisierung gelöst. Das Anwendungsbeispiel ist das Kaltharzverfahren, ein traditionell in Handarbeit bewältigter Herstellungsprozess für Gussformen. In diesem Prozess spielt die Fachkraft eine zentrale Rolle (z. B. durch ihr Prozesswissen und ihre Expertise), während die (intelligente) Automatisierung –geführt und überwacht durch die Fachkraft– anfallende physische Aktionen ausführt. Dies wird durch experimentell ermit- telte qualitäts-beschreibende Prozessgrößen erreicht, die eine in-prozess Rückführung zum Bedienpersonal ermöglichen. Prozessführungsassistenz ist basierend auf die Formalisierung der Mensch-Automation-Interaktion gegeben. Durch die Bestimmung von situativen Informationen hoher Wichtigkeit aus dem resultierenden Mensch-Automation-System Modell bezogen auf das aktuelle Prozessziel, wird das bestehende Prozessmodell zur Überwachung und Prozessführungsassistenz des Gesamtprozesses genutzt. Die Gestaltung der Mensch-Maschine-Schnittstelle basiert auf einer detaillierten Analyse des Handarbeitsprozesses und ist als direkte, intuitiv bedienbare, markerbasierte Interaktionstechnik realisiert. Das integrierte Mensch-Automation-System sowie die zugehörige Mensch-Maschine-Schnittstelle inklusive Prozessführungsassistenzfunktionen wurden initial evaluiert. Die erzielten Ergebnisse werden hinsichtlich des individuellen, fachkraftabhängigen Prozesswissens und der Reproduzierbarkeit für den Ausblick diskutiert