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.

翻译：制造装配任务在复杂性和自动化程度上存在差异。然而，由于某些装配操作的复杂性，实现完全自动化既具挑战性又效率低下。通过结合人类劳动优势与机器人能力的人机协同工作，可成为提升效率的解决方案。本文提出CT基准测试——一套旨在促进人机协同装配场景测试与评估的基准模型集。该基准通过装配时间与人类工作量等指标，对人工流程与自动化流程进行比较。模型集组件可通过最常见的装配任务进行组装，每项任务均具有不同难度等级。CT基准测试在设计上重点关注其在人机协同环境中的适用性，旨在确保实验的可复现性与可重复性。实验在三种不同设置（人工、自动与协同）下评估装配性能，测量了与装配时间及操作人员工作量相关的指标。结果表明：协同方法相较于全人工装配耗时更长，增幅达70.8%。但根据NASA-TLX问卷反馈，用户报告了更低的总工作量，以及降低的心理需求、体力需求与努力程度。