# Construction of Robotics and Application of the Optical-Flow Algorithm in Determining Robot Motions
## 5 Conclusions  
In this study, an optical-flow algorithm was proposed to determine the movement state of the robot in real time. The algorithm shows a simple and inexpensive but highly effective performance in the initial process of determining an object. Additionally, the transferring speed of the image is sufficiently high with small errors. The tests are conducted for three models with different sizes to study the effect of length on the uncertainty. The main conclusion of this study is as below.  
* The algorithm shows a high ability to determine the position, velocity, and angle of the robot in space. The results can be applied to other objects in the same frame when a calibration process is performed. The average error of velocity was 7.9%, and the angle was 1.25\({}^{\circ}\). This error becomes high when the robot moves far from the camera. However, the uncertainty is less sensitive to the length of the model and trajectory. In  
Figure 28: Velocity error and angular displacement error when the object moves in a sinusoidal pattern with a set speed of 10 cm/s.  
a complicated trajectory, both measurements in robot and optical-flow results show highly accurate results with an average error of 8.9% in velocity and 2.40% in angle. * A correlation function was built for different objects. It is shown that the function is simple but is highly effective in determining the object. Additionally, it can be featured for other objects with a calibration process in advance. * Systematic robots and measurements were successful in design for the testing process. The robot with a telecommunication system can be applied in real application. The communication system allows detection of the positions of the robot in real time with highly accurate results. * The measurement systems developed in the current study can be used as the initial process of determining the state of the robot. The real test and application will be developed in the future to confirm the efficiency of the system. Additionally, a communication system should be developed to link the positions of the robot by the camera and a highly advanced, expensive technique such as laser to improve the accuracy of the measurements.  
Although the algorithms and robot systems were designed, increasing the accuracy and improving the computer system for real-time identification should be considered in further study. Additionally, an algorithm for detecting the models with changing height during the movement should be developed. Those are important tasks for further study.  
Conceptualization, A.V.N. and T.H.T.; methodology, A.V.N. and V.T.H.; software, A.V.N. and V.T.H.; validation, A.V.N. and V.T.H.; formal analysis, A.V.N. and T.H.T.; investigation, A.V.N. and V.T.H.; resources, A.V.N. and T.H.T.; data curation, A.V.N. and V.T.H.; writing--original draft preparation, A.V.N. and V.T.H.; writing--review and editing, A.V.N. and T.H.T.; visualization, A.V.N. and T.H.T.; supervision, A.V.N. and T.H.T.; project administration, A.V.N. and T.H.T.; funding acquisition, A.V.N. and V.T.H. All authors have read and agreed to the published version of the manuscript.  
This research is funded by Le Quy Don Technical University Fund under the grant number \"2022.QHT.05\".  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
We would like to thank Le Quy Don Technical University for its support in the paper.  
The authors declare no conflicts of interest.