## 4 Experimental Results and Discussion
### Experimental Time Consumption  
The time consumed in the experiment was the main consideration, but the percentage of time consumed in each stage of the experiment is also worth discussing. The percentage of time is mainly used to analyze the internal composition of the experimental model. It can be seen from Figure 15 that Group A completed the experiment the first time in an average of 15.2 min, while Group B took 6.2 min. For the second time, Group A completed  
Figure 14: A comparison between conventional methods and the proposed method. (**A**) Button states 1–6 = up, down, left, right, forward, and back. States 7–12 = + Roll, \(-\) Roll, +Pitch, \(-\)Pitch, +Yaw, and \(-\)Yaw. States 13–14 = Grab, Release. (**B**) Eye–hand states 1–4 = Recognize, Indication, Capture, and Mapping. Figure 13: Robot trajectory of our model combining automatic and mapping parts. the experiment in an average of 13.5 min, while Group B took 5.9 min. Both times, Group A was slower than Group B. Figure 16 shows that in Group A, for the first time, 28.4% of the time was used to move the robot to the object. Grabbing objects took 22.9%, moving objects to the target position took 27.9%, and the assembly operation took 20.8%. The second time, moving the robot to the object took 29.3% of the time, grabbing objects took 22.4%, moving objects to the target position took 29.0%, and the assembly operation stage took 19.3%. For the first time for Group B, indicating the object and end position took 23.2% of the time, 55.3% was occupied by capture, moving the object to the target position, and mapping assembly of the object took 21.5%. The second time, indicating the object and end position occupied 22.0% of the time, 58.5% of the time was occupied by the capture stage, and the mapping assembly stage took 19.4%. For grasping and moving, handing objects to the robot arm significantly reduced the overall cooperative assembly time. The importance of moving the object to the target position was weakened. It can be considered that the interaction mode combining eye and hand commands was more efficient. This may be because the traditional interaction, grab, and placement stages were all controlled by a human. Users are not adaptable to changing the 6-DOF of the robot by key. For operators will think about which degree of freedom is appropriate and whether the key is correct, which reduces the assembly efficiency. During eye-hand control,  
Figure 16: Comparisons of the percentages of time occupied by each assembly state. Figure 15: A comparison of the assembly states and times of Groups A and B.  
users just watch the screen to perform manual control, a little like in Figure 13. As for the results, the first concern of users is the interaction time. The eye-hand interaction mode is more efficient than the controller interaction mode, which shows that the interaction mode of indication and mapping is feasible. Users can also change the mode of indication, such as voice or touchscreen, and compare the interaction efficiencies of the proposed and traditional controllers. Secondly, the interaction state is divided, which clearly shows the proportion of time consumed in the experiment, and readers can further optimize certain states. There have been some similar human-computer cooperation studies in which humans control robots through a data glove and the robot's complete trajectory correction and obstacle avoidance [50]. In comparison, data glove stability may be better; however, it is inconvenient to wear. Besides, collecting data from the upper body to control a robot [51]. The whole action needs to be completed by the user, and the user's burden is high. In contrast, our interaction model can be switched between instruction and mapping modes, which reduces the user burden. From the perspective of interactive experience, this paper has some shortcomings; for example, users cannot feel the grip force.