# Parameterization and Design of Telepresence Robot to Avoid Obstacles
## 5 Conclusions  
A control board and a voltage regulator for the steering servo motor are also required to maneuver the robot successfully. Fundamentally different models were successfully developed, built and tested. A new motor mount had to be created for the drive, to which two motors could be attached. It was necessary because the existing motor alone could not generate enough torque to move to and maintain the robot's stable operating points. In addition, the brackets for the ten distance sensors had to be designed.  
In the proposed research work, we designed speed and position control. The speed control loop was designed using the PID controller. An algorithm was developed for the state machine to avoid obstacles. It was a complex task that needed precise analysis and design. In the case of the robot, the high-level controller was a mini-computer running the application program. To this end, controlled test drives were undertaken, and the results were evaluated. Finally, the ideally adjusted robot was checked for its desired behavior.  
The robot was tested over the step responses and the self-localization to test the maneuverability and control. It was seen from the results that the robot followed the given path with an ignorable number of hits. The robot's calculated driven trajectory was measured and provided very precise results. It can be seen that the target path was maintained quite well.  
As the summary suggests, software drivers should be written for the hardware. The timing of these drivers probably needs to be adjusted. So far, no driver has been developed for the SD card. This development would likely also fill a separate research task. In addition, the soldering pads on the control board used are attached the wrong way around so that the SD card slot cannot be attached. There is also no driver for the real-time clock. Another problem, which became apparent in the course of the research, is the correct self-localization of the robot. So far, it estimates its position by gradually adding up the distance travelled in the direction of orientation. First, the arc length travelled is used as the tangent length of the motion vector. It is also tolerable for small changes in direction. However, the systematic error accumulates over time when cornering. A correct solution to this problem would be quite complex and likely bring little improvement.  
Conceptualization, A.A. and A.S.; methodology, M.N.K.; software, M.N.K.; validation, A.A., A.S. and M.N.K.; formal analysis, M.N.K.; investigation, A.S.; resources, A.S.; writing--original draft preparation, M.N.K.; writing--review and editing, A.S.; supervision, M.N.K.; funding acquisition, A.A. All authors have read and agreed to the published version of the manuscript.  
The authors extend their appreciation to the Deputyship for Research and Innovation, the Ministry of Education in Saudi Arabia for funding this research work through the project number IF2/PSAU/2022/01/22378.  
Not applicable.  
The authors acknowledge the support of the Ministry of Education in Saudi Arabia, and Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Examples**} & **Trial 1** & **Trial 2** & **Trial 3** & **Mean** \\ \cline{2-5}  & **Distance** & **Distance** & **Distance** & **Distance** \\  & **(m)/Time (s)** & **(m)/Time (s)** & **(m)/Time (s)** & **(m)/Time (s)** \\ \hline
1 st trip & 8.67 m/92 s & 9.79 m/98 s & 9.13 m/95 s & 9.2 m/95 s \\
2 nd trip & 9.89 m/90 s & 9.13 m/87 s & 8.13 m/88 s & 9.0 m/88 s \\
3 rd trip & 9.13 m/98 s & 9.05 m/89 s & 9.05 m/75 s & 9.0 m/87 s \\
4 th trip & 8.44 m/87 s & 9.84 m/72 s & 9.94 m/61 s & 9.3 m/73 s \\ \hline \hline \end{tabular}
\end{table}
Table 4: Experimental data for different trials.