# Seamless Human-Robot Collaborative Assembly Using Artificial Intelligence and Wearable Devices
## 5 Conclusions and Future Work  
This work discusses an AI system that recognizes the actions being performed by operators inside a human-robot collaborative cell, analyzes the ergonomics, and adapts the robot behavior according to the needs of the task and the preferences of the operator, improving ergonomics and operator's satisfaction. The demonstration in an industrial case study from the elevator production sector has revealed possible enhancement in the cycle time (by 6%), amelioration of ergonomic factors in 80% of the samples, and a quite high operator acceptance. Finally, the successful use of, non-so widespread, high payload collaborative robotics has been demonstrated, which, unlike low payload cobots, are more capable to supplement humans in performing strenuous tasks.  
Nowadays, the industry is slowly moving from either manual production or totally automated production to hybrid solutions. The current implementations utilize mainly  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & \multirow{2}{*}{**Cycle Time (s)**} & \multicolumn{3}{c}{**Ergonomics Score**} & \multirow{2}{*}{**Satisfaction**} & \multirow{2}{*}{**LPM**} \\  & & & **(Max RULA Value)** & & & **(Higher Is** \\  & & & **(_Lower Is Better_)** & & **(_Higher Is_ \\  & **Before** & **After** & **Before** & **After** & **(_Better_)** & **Curve (_Cycles_) \\ \hline Operator 1 & 137 & 140 & 2/6 & 2/6 & 3.2/5 & 2 \\ Operator 2 & 186 & 175 & 2/6 & 3/6 & 3.8/5 & 4 \\ Operator 3 & 154 & 141 & 3/6 & 2/6 & 4.8/5 & 6 \\ Operator 4 & 192 & 177 & 4/6 & 2/6 & 4.2/5 & 5 \\ Operator 5 & 169 & 155 & 3/6 & 2/6 & 4.2/5 & 4 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental results.  
Figure 6: High-payload collaborative workstation paradigm.  
low payload robots and offer limited cognition of process and surroundings, impacting negatively the operator's acceptance of such technologies. The proposed solution aspires to bring industry one step closer to wide adoption of human-robot collaborative solutions, by having a robot working seamlessly next to a human. According to the proposed paradigm, the human has the leading role while the robot assists him/her non-intrusively, bending its behavior around him/her. Such an approach leads to higher satisfaction rates, and consequently to higher acceptance of hybrid production solutions. The methodology can be applied in low payload as well as high payload applications.  
Future work will aim at the implementation of algorithms to capture more complex--non-assembly-based--human actions in order for the system to either ignore them or plan for countering their effects. Additionally, the virtual mannequin used for simulations will have the capability to adapt to the exact dimensions of the operator, so more accurate simulations of ergonomics can be achieved. Moreover, exploration of additional wearable hardware, such as IMUs on a wristband, for highly granular detection of human posture is needed. Finally, extensions to guarantee the operator's safety in a certified way must also be implemented.  
Conceptualization, N.D., G.M. and S.M.; methodology, N.D., G.M. and S.M.; software, T.T. and N.Z.; validation, N.D., T.T., N.Z., G.M. and S.M.; writing--original draft preparation, N.D. and G.M.; writing--review and editing, N.D. and G.M. All authors have read and agreed to the published version of the manuscript.  
This research has been supported by the EU project \"SHERLOCK Seamless and safe human-centered robotic applications for novel collaborative workshops\". This project has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement No 820689.  
Not applicable.  
Data Availability StatementData is contained within the article.  
The authors declare no conflict of interest.