  Manufacturing assembly tasks can vary in complexity and level of automation.
Yet, achieving full automation can be challenging and inefficient, particularly
due to the complexity of certain assembly operations. Human-robot collaborative
work, leveraging the strengths of human labor alongside the capabilities of
robots, can be a solution for enhancing efficiency. This paper introduces the
CT benchmark, a benchmark and model set designed to facilitate the testing and
evaluation of human-robot collaborative assembly scenarios. It was designed to
compare manual and automatic processes using metrics such as the assembly time
and human workload. The components of the model set can be assembled through
the most common assembly tasks, each with varying levels of difficulty. The CT
benchmark was designed with a focus on its applicability in human-robot
collaborative environments, with the aim of ensuring the reproducibility and
replicability of experiments. Experiments were carried out to assess assembly
performance in three different setups (manual, automatic and collaborative),
measuring metrics related to the assembly time and the workload on human
operators. The results suggest that the collaborative approach takes longer
than the fully manual assembly, with an increase of 70.8%. However, users
reported a lower overall workload, as well as reduced mental demand, physical
demand, and effort according to the NASA-TLX questionnaire.
