# Implementation and Evaluation of Dynamic Task Allocation for Human-Robot Collaboration in Assembly
## 5 Conclusions  
This article presented an approach for dynamic task allocation and its implementation into a block-based software framework for integrated process creation and execution. By combining intuitive block-based programming with the proposed dynamic task allocation approach, which requires only basic information input, we facilitate both the creation and planning of dynamic HRC processes as well as their adaptation to different use-cases. The proposed dynamic task allocation approach is evaluated in an extensive user study and compared to manual assembly as well as collaborative assembly with static task allocation. With the design of the evaluation study, we suggest a comprehensive systematic framework for evaluation of HRC assembly processes. The user study resulted in new insights for collaborative robotics concerning the potentials of dynamic task allocation. For one thing, dynamic task allocation reduces the overall cycle time compared to both manual assembly and static task allocation, given a high degree of parallelism in the assembly processes. For another, it increases HRC fluency in general and, in particular, in the event of deviations or delays during the assembly process. In our future research, we aim to further assist both the planner and the operator by implementing an automatic import function for the planner that creates the dynamic HRC process based on the process input data to further simplify and quicken the process creation. For improved support of the operator, we focus on the implementation of the suggestions for system improvement obtained in the user study. Further research needs are a long-term study of dynamic task allocation over multiple weeks with expert workers in real industrial environments, the investigation of the influence of different time points of process delays, and the analysis of errors in different task allocation modes for complex industrial components. The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app122412645/s1](https://www.mdpi.com/article/10.3390/app122412645/s1)]([https://www.mdpi.com/article/10.3390/app122412645/s1](https://www.mdpi.com/article/10.3390/app122412645/s1)), Raw data S1: Videos showing the exemplary demonstration of the process execution by the author [PERSON] in all six combinations of task allocation modes and number of components; Raw data S2: Log files of the process execution for all subjects; Raw data S3: Responses of all subjects to questionnaires. Conceptualization, C.P. ; methodology, C.P. ; software, C.P., D.N., B.V. and M.S. ; validation, C.P. and D.N. ; formal analysis, C.P. ; investigation, C.P. and E.M.; resources, C.P. and M.F. ; data curation, C.P. and E.M.; writing--original draft preparation, C.P. ; writing--review and editing, C.P., D.N., B.V., E.M. and M.F. ; visualization, C.P. ; supervision, C.P. and M.F. ; project administration, C.P. and M.F. ; funding acquisition, C.P. and M.F. All authors have read and agreed to the published version of the manuscript. This research was funded by the European Regional Development Fund (EFRE) and the Bremer Aufbau-Bank (BAB), Germany, as part of the project \"KoMILO--Context-dependent, AI-based interface for multimodal human-machine interaction with technical logistics systems\", grant number FuE0637B. The APC was funded by the Staats- und Universitatsbibliothek Bremen (SuUB), Germany. Ethical review and approval were waived for this study due to anonymized data collection. Moreover, the focus is not on humans but on the technical system.