## 2 Background
### Work Design in HRCS
#### 2.1.1 Manual Setting  
Manual setting tasks were found to be the most popular within HRCS assembly applications. The adjustment of workpieces by hand or manual tools has been seen  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Component** & \
\begin{tabular}{c} **Manual** \\ **Setting** \\ \end{tabular} & **Screwing** & **Mounting** &
\begin{tabular}{c} **Precise** \\ **Positioning** \\ \end{tabular} \\ \hline Interaction levels & & & & \\ Sequential & ✓ & ✓ & & \\ Simultaneous & ✓ & & & \\ Supportive & ✓ & ✓ & ✓ & ✓ \\ \hline Work roles & & & & \\ Follower & ✓ & & & \\ Peer & ✓ & ✓ & ✓ & ✓ \\ Leader & ✓ & ✓ & ✓ & ✓ \\ \hline Comm. interfaces & & & & \\ Legacy & ✓ & ✓ & ✓ & \\ Intuitive & ✓ & ✓ & ✓ & ✓ \\ \hline Safety controls & & & & \\ Collab. integrity & ✓ & ✓ & ✓ & ✓ \\ Robot integrity & ✓ & ✓ & ✓ & ✓ \\ Human integrity & ✓ & ✓ & ✓ & ✓ \\ \hline _References_ & [10; 13; 14; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42] & [16; 43; 44; 45; 46; 47; 48; 49; 50; 51; 52; 53; 54; 55; 56; 57; 58] & [15; 45; 50; 59; 60; 61; 62; 63] & [11; 64; 65; 66; 67; 68] \\ \hline \hline \end{tabular}
\end{table}
Table 1: HRCS work design for assembly applications.  
Figure 1: Work design framework for human–robot collaborative systems.  
as convenient for collaborative environments, especially in sequential and supportive scenarios. For instance, in wire harness assembly, robots are assigned to operate devices such as taping pistols [13], vacuums [14], or dual-finger grippers [10] by the instruction of human operators. This kind of task represents a complex manipulation of cables and wires that remain beyond the proficiency of robots. Thus, the inclusion of a robot companion is centered on handling the workpieces where conductors are to be installed (i.e., vehicle dashboards, engine blocks, or connection plates), improving the working posture and biomechanical load of human operators [26].  
Alternatively, small, light-weight workpieces comprised in the assembly of automotive industry elements, as in the case of transmissions [27], drive shafts [28], or center consoles [31], are highly suitable for manipulation by collaborative robots. Manual setting in this context relies on the handover of objects where human operators instruct robots to grasp certain workpieces in a desired succession. Tasks deriving from the metallurgic [33], electronic [41], and oil industries [29] exhibit a supportive interaction similar to the ones described for auto-parts production. Additionally, it is worth noting the variety of materials like metals, lumbers, and plastics being tested for collaborative manipulation within laboratory environments [30; 32; 35].  
For most cases, manual setting demands human operators to have some control of the task pace as confirmation of readiness to receive a workpiece and proceed with further activities is required. This coordination is especially important for work designs that include dual-arm robots [36; 42]. In this sense, legacy communication by physical [25] and digital buttons [38] is a simple manual mechanism that satisfies the collaborative workflow of both sequential and supportive interactions.  
Amid assembly applications, manual setting tasks were the only ones that employed follower role dynamics for human operators [34; 39; 40]. Moreover, it has been observed that this passive role is better exploited in sequential scenarios. Robots present written instructions to human operators by means of a monitor, pausing the task until confirmation of readiness to proceed is obtained. A more intuitive approach to trigger robot instructions without the need for explicit confirmation is based on situational awareness, where robots can visually validate that a preceding activity has been completed by human operators or themselves [37].