This literature review presents a comprehensive analysis of the use and potential application scenarios of collaborative robots in the industrial working world, focusing on their impact on human work, safety, and health in the context of Industry 4.0. The aim is to provide a holistic evaluation of the employment of collaborative robots in the current and future working world, which is being increasingly driven by the automation and digitization of production processes, and which can be outlined using the term Work 4.0. In this context, the forms of work organization, and the demands and impacts on humans are changing profoundly. Collaborative robots represent a key technology to aid the transition. The review utilizes expert interviews for identifying relevant keywords, and follows the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) framework to evaluate peer-reviewed literature between 2002 and January 2022. The analysis includes forms of interaction, and the distribution of roles, control interfaces, safety procedures, and ergonomics and health. In addition, the review offers a heatmap displaying the research topics of 715 publications for each year, and a database of these publications in BibTeX format that is publicly accessible. The review highlights the challenges, potential benefits, and trends of using collaborative robots in the industrial working world, emphasizing the importance of a careful evaluation of their impact on human work, safety, and health. It offers a tool set for researchers and practitioners to further explore and evaluate the employment of collaborative robots in Work 4.0.

While human–robot collaboration is already integrated in industrial and service robotics applications, it is only used with able-bodied workers. However, collaboration through assistive robots is a major driver toward the inclusion of people with disabilities, which was demonstrated in recent research projects. Currently, inclusive robot workplaces have to be customized toward the work process and the individual needs of the person. Within, robots act along a fixed schedule and are not able to adapt to changes within the process or the needs of the interacting person. Hence, such workplaces are expensive and unappealing for companies of the first labor market, and do not realize the full potential of the technology. In this work, we propose a generalized approach toward the inclusion of people with disabilities with collaborative robots. To this end, we propose a system that analyzes the in situ capabilities of a person using a two-stage reasoning approach. The methodology is based on an ontology that allows the matchmaking of individual capabilities with process requirements. Capabilities are modeled in two time frames, through which fast (e.g., fatigue) and slow effects (e.g., worsening of illness) become distinguishable. The matchmaking is used in task allocation to establish high-level control over the assistive system. By this approach, inclusive workplaces become autonomously adaptive to the in situ capabilities of the individual person, without the need for customization. Therefore, collaborative workplaces become not only inclusive, but a contributor toward a labor market for all.

Leg tracking is an established field in mobile robotics and machine vision in general. These algorithms, however, only distinguish the scene between leg and nonleg detections. In application fields like firefighting, where people tend to choose squatting or crouching over standing postures, those methods will inevitably fail. Further, tracking based on a single sensor system may reduce the overall reliability if brought to outdoor or complex environments with limited vision on the target objectives. Therefore, we extend our recent work to a multiposture detection system based on laser and radar sensors, that are fused to allow for maximal reliability and accuracy in scenarios as complex as indoor firefighting with vastly limited vision. The proposed tracking pipeline is trained and extensively validated on a new data set. We show that the radar tracker reaches state‐of‐the‐art performance, and that laser and fusion tracker outperform recent methods.

The recognition of human activities from video sequences and their transformation into a machine-readable form is a challenging task, which is the subject of many studies. The goal of this project is to develop an automated method for analyzing, identifying and processing motion capture data into a planning language. This is performed in a cooking scenario by recording the pose of the acting hand. First, predefined side actions are detected in the dataset using classification. The remaining frames are then clustered into main actions. Using this information, the known initial positions and virtual object tracking, a machine-readable planning domain definition language (PDDL) is generated.

In the EU project SHAREWORK, methods are developed that allow humans and robots to collaborate in an industrial environment. One of the major contributions is a framework for task planning coupled with automated item detection and localization. In this work, we present the methods used for detecting and classifying items on the shop floor. Important in the context of SHAREWORK is the user-friendliness of the methodology. Thus, we renounce heavy-learning-based methods in favor of unsupervised segmentation coupled with lenient machine learning methods for classification. Our algorithm is a combination of established methods adjusted for fast and reliable item detection at high ranges of up to eight meters. In this work, we present the full pipeline from calibration, over segmentation to item classification in the industrial context. The pipeline is validated on a shop floor of 40 sqm and with up to nine different items and assemblies, reaching a mean accuracy of 84% at $0.85$ Hz.

Skilled labor shortage is a prominent challenge in the world of work. Meanwhile, age-related disabilities or injury lead to at least temporary performance limitations, which make people unfit to work. Consequently, even less workers are available. By employing human-robot teams, the performance of these people may be restored. This requires a good artificial understanding of the human’s capabilities, as generic robot behavior is not feasible with the highly individualized manifestations of disability. We present an approach that allows the robot to autonomously assess human capabilities based on standards from occupational medicine. The method does not only indicate the presence/absence of capabilities, but gives them a discrete rating. This allows the robot to better define its own behavior as a mixture of supportive actions based on gaps in the detailed capabilities.