# Implementation and Evaluation of Dynamic Task Allocation for Human-Robot Collaboration in Assembly
## 4 Discussion  
The conducted user study demonstrated the potentials of HRC for assembly processes that allow a certain degree of parallelism in the execution of sub-processes. Within the study, we found a reduction in cycle time of approximately 20% compared to manual assembly. This is relatively consistent with the results of [38], who reported a reduction in cycle time of approximately 25% for a similar amount of process steps (16 tasks in [38]; 14 tasks in conducted study for assembly of one component). Another interesting finding is that dynamic task allocation can only exploit its potential at a certain degree of parallelism (process flexibility) in the assembly process to outperform static task allocation. Assembly processes that do not have these degrees of flexibility may be adapted through the parallel assembly of a higher number of components. This also avoids the reported problem of operators having to adjust their movements, as the resources can perform their respective tasks in parallel on several components, which in turn reduces cycle time by reducing waiting times. Both the quantitative and qualitative results of the study further indicate a better fluency of the HRC assembly when using dynamic task allocation. In particular, in the event of delays during the process execution of one resource, the other resource is not forced to wait, but can instead continue with the execution of remaining unfinished tasks. This is especially reflected in a significantly higher concurrent activity time in dynamic task allocation compared to static task allocation. This characteristic also enables dynamic task allocation--for processes with a correspondingly large number of sub-processes that can be executed by both humans and robots--to flexibly adapt to the individual worker, independent of its task execution performance, without the need for specific planning or programming. Consequently, even performance fluctuations during the day can be compensated. Figure 22: Subjects’ ranking of preferred task allocation mode. Furthermore, some subjects also commented that they were stressed if the robot had to wait for them, which underlines the importance of a high concurrent activity time and a low robot wait time. In terms of quantitative analysis, there remains a need for research to investigate how exactly delays in the process can be absorbed by each task allocation mode. A study specifically designed for this purpose should be conducted as future research, which systematically takes into account different time points of interruption during the process with a large number of subjects and even considers longer durations of delay than 90 s.  
A reduction in the planning effort can generally be noted when using dynamic task allocation with the presented system. For realizing assembly processes in this task allocation mode with the proposed software framework, only a transfer of basic input information (process times, assembly priority graph, and task classification regarding the feasibility for execution by humans or robots) is required, which is typically available or would also have to be collected as a first step in static planning. Compared to static task allocation, neither detailed prior process scheduling nor an experimental fine-tuning of the planned processes are required. With regard to the repeatability of the conducted study, identical conditions can be maintained with respect to the experimental setup and study procedure. Due to the characteristics of a user study, the individual absolute execution times and answers to the questionnaires depend on the respective performance of each subject, or their current daily form. Nevertheless, considering the number of subjects, it can be assumed that the core results found would remain the same overall if the experiment were repeated. Limitations of the study particularly include the influence of learning effects, which will have occurred within the study despite randomized selection of the execution order of task allocation modes in the individual runs.