Growing interest in industrial human-robot interaction (HRI) applications makes it necessary to look deeper into the design of systems, where humans collaborate, interact, or at least coexist with industrial robots. This study investigates the influence of the trajectory of an industrial robot's Tool Center Point (TCP) on user well-being as well as human performance in the cooperative scenario HRI. Therefore, a study with a total of 19 participants was conducted. The subjects had to perform several tasks (visually interacting with the robot and performing an audio n-back task), while the robot made different motions in their vicinity. Results show that variable, i.e. non predictable, robot motions lead to reduced human well-being and performance. Consequently, non-predictable motions are not suited for use in HRI. Well-being and performance can be enhanced if the robot moves directly on a straight line from start to finish.

Modeling and predicting human behavior is indispensable when industrial robots interacting with human operators are to be manipulated safely and efficiently. One challenge is that human operators tend to follow different motion patterns, depending on their intention and the structure of the environment. This precludes the use of classical estimation techniques based on kinematic or dynamic models, especially for the purpose of long-term prediction. In this paper, we propose a method based on Hidden Markov Models to predict the region of the workspace that is possibly occupied by the human within a prediction horizon. In contrast to predictions in the form of single points such as most likely human positions as obtained from previous approaches, the regions obtained here may serve as safety constraints when the robot motion is planned or optimized. This way one avoids collisions with a probability not less than a predefined threshold. The practicability of our method is demonstrated by successfully and accurately predicting the motion of a human arm in two scenarios involving multiple motion patterns.

Human-machine systems with shared authority can be observed in different domains of assistance systems. This article creates a taxonomy of the most important aspects of human-machine cooperation in five layers: intention, modes of cooperation, allocation, interfaces and contact. This is investigated with help of driver assistance and HumanRobot Interaction. Furthermore, a perspective for possibilities of cross-domain generalization is given. Zusammenfassung Mensch-Maschine-Systeme mit geteilter Autorität entwickeln sich in vielen Domänen der Assistenzsysteme. Dieser Beitrag stellt die wichtigsten Aspekte der Mensch-MaschineKooperation in fünf Ebenen der Intention, Kooperationsmodi, Allokation, Schnittstellen und Kontakt dar. Dies wird anhand der Beispiele Fahrerassistenz und Mensch-Roboter-Interaktion beleuchtet. Weiterhin wird ein Ausblick auf Möglichkeiten zur domänenübergreifenden Generalisierung gegeben.

Human workers and industrial robots both have specific strengths within industrial production. Advantageously they complement each other perfectly, which leads to the development of human-robot interaction (HRI) applications. Bringing humans and robots together in the same workspace may lead to potential collisions. The avoidance of such is a central safety requirement. It can be realized with sundry sensor systems, all of them decelerating the robot when the distance to the human decreases alarmingly and applying the emergency stop, when the distance becomes too small. As a consequence, the efficiency of the overall systems suffers, because the robot has high idle times. Optimized path planning algorithms have to be developed to avoid that. The following study investigates human motion behavior in the proximity of an industrial robot. Three different kinds of encounters between the two entities under three robot speed levels are prompted. A motion tracking system is used to capture the motions. Results show, that humans keep an average distance of about 0,5m to the robot, when the encounter occurs. Approximation of the workbenches is influenced by the robot in ten of 15 cases. Furthermore, an increase of participants' walking velocity with higher robot velocities is observed.