# Seamless Human-Robot Collaborative Assembly Using Artificial Intelligence and Wearable Devices
## 4 Case Study  
To demonstrate the aforementioned approach, a used case derived from the elevator production sector was selected. Specifically, at the workstation of interest, the cab door panel hangers and 11 subcomponents are pre-assembled. Currently, all material handling and assembly operations are performed manually by an operator multiple times per shift. The heavy weight of hangers (up to 22 kg) and the need for two-sided access cause extensive ergonomic issues and symptoms such as back pains, hand tendonitis, and others. This is the primary reason why operators with restrictions cannot work in this station.  
The first high payload (170 kg) collaborative robot at the market (COMAU AURA [31]) is used as a smart work holding device, taking care of the arduous task of positioning the assembly. This allows operators to perform delicate assembly actions in an effective, pleasant, and ergonomic way. Based on the operator position, pace, and the sequence of assembly operation, the robot automatically adapts its position, rotating the hanger in space and presenting it to the operator in a convenient way (variation of the programmed frame (R) by \(\Phi\) and \(\Delta\)Z to frame (O) shown in Figure 6). In other words, a change of the current paradigm, where the part is static and the operator moves and bends around it to do the different operations, is proposed putting the human in the center of the operation and the robot presenting the part to him.  
To evaluate the effectiveness of our approach, an experimental setup involving 5 operators in total took place (4 males, 1 female), varying in height from 1.56-1.92 cm and in age from 23-44 years old. Each of the subjects was asked to assemble the hanger in collaboration with the high-payload robot in two experimental configurations: (a) without AI support; the robot holds the panel at a pre-programmed stationary position, while the operator performs assembly with the help of AR instructions, confirming the completeness of each step through HMIs and hand gestures, and (b) with the utilization of the APM, EIM, and LPM modules; the robot automatically adapts its pose to operator's needs and preferences when needed, without having to provide feedback for his/her actions. Each operator repeated 10 times each of the experimental configuration (total of 100 tries for the 5 operators). For each repetition, the cycle time and ergonomic score KPIs were monitored, while at the end of the experimentation the operators filled in questionnaires to capture their subjective feedback regarding the effectiveness of each of the modules of our approach. The results are summarized in Table 1.  
Figure 5: LPM and EIM functions.  
The described approach had a positive impact on the cycle time, as most operators managed to achieve better cycle time by using the modules of this study (reduction of \(\sim\)6%). Regarding ergonomics, the EIM managed to improve the maximum ergonomic values for 4/5 operators, except from the first subject whose height fit well the pre-programmed pose of the robot. All the subjects indicated above-average satisfaction for the developed technology. Finally, for most of the subjects, the LPM was able to adapt to operator needs and preferences after four cycles, as limited intervention was observed afterward, either by humans or by the EIM.