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Human–robot collaboration is becoming increasingly important in industrial assembly. In view of high cost pressure, resulting productivity requirements, and the trend towards human-centered automation in the context of Industry 5.0, a reasonable allocation of individual assembly tasks to humans or robots is of central importance. Therefore, this article presents a new approach for dynamic task allocation, its integration into an intuitive block-based process planning framework, and its evaluation in comparison to both manual assembly and static task allocation. For evaluation, a systematic methodology for comprehensive assessment of task allocation approaches is developed, followed by a corresponding user study. The results of the study show for the dynamic task allocation on the one hand a higher fluency in the human–robot collaboration with good adaptation to process delays, and on the other hand a reduction in the cycle time for assembly processes with sufficiently high degrees of parallelism. Based on the study results, we draw conclusions regarding assembly scenarios in which manual assembly or collaborative assembly with static or dynamic task allocation is most appropriate. Finally, we discuss the implications for process planning when using the proposed task allocation framework.

The quickly rising development of autonomous vehicle technology and increase of (semi-) autonomous vehicles on the road leads to an increased demand for more sophisticated human–machine-cooperation approaches to improve trust and acceptance of these new systems. In this work, we investigate the feeling of discomfort of human passengers while driving autonomously and the automatic detection of this discomfort with several model approaches, using the combination of different data sources. Based on a driving simulator study, we analyzed the discomfort reports of 50 participants for autonomous inner city driving. We found that perceived discomfort depends on the driving scenario (with discomfort generally peaking in complex situations) and on the passenger (resulting in interindividual differences in reported discomfort extend and duration). Further, we describe three different model approaches on how to predict the passenger discomfort using data from the vehicle’s sensors as well as physiological and behavioral data from the passenger. The model’s precision varies greatly across the approaches, the best approach having a precision of up to 80%. All of our presented model approaches use combinations of linear models and are thus fast, transparent, and safe. Lastly, we analyzed these models using the SHAP method, which enables explaining the models’ discomfort predictions. These explanations are used to infer the importance of our collected features and to create a scenario-based discomfort analysis. Our work demonstrates a novel approach on passenger state modelling with simple, safe, and transparent models and with explainable model predictions, which can be used to adapt the vehicles’ actions to the needs of the passenger.

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