# Simple Ultrasonic-Based Localization System for Mobile Robots
## 5 Conclusions  
The presented system, designed for indoor localization of mobile robots and inventory management (Figure 1), meets the requirements of RTLS, has a sampling frequency >10 Hz, and is simple, easily deployable, and cost-effective. It also fulfills the assumption of measurement repeatability in individual axes <20 mm. From the measured and calculated results, the worst outcome within the valid range was 14.69 mm for the x-coordinate and 10.87 mm for the y-coordinate. Results for the z-axis are less significant due to the nature of mobile robot movement, where this axis is mostly unnecessary as its value remains constant. However, further verification of the proposed system is necessary, involving measurements in precise positions focusing on the deviation from the actual position, to determine deviations from actual positions at measurement points. It is necessary to construct a stand that allows precise positioning of the receiver and enables the attachment of tags with high precision relative to each other and to the measurement points. Additionally, verification of the system during changes in movement and its detection within the valid area is required. Therefore, subsequent studies delved into the subject of dynamic testing, where the variability of conditions in complex indoor spaces can affect signal transmission due to reflection or absorption of ultrasonic waves. Moving objects, such as people or other robots, can distort data due to Doppler effects, requiring data processing algorithms to adapt to changes. Precise time synchronization becomes critical, as delays in signal processing significantly affect localization accuracy. Challenges associated with multiple signal paths require the development of methods to distinguish the primary signal from reflections. Also, there is an increasing need for faster data updates for effective operation in dynamic conditions.  
Conceptualization, M.S. and M.G.; methodology, M.S. and R.J.; software, M.S.; validation, J.S.; data curation, M.G.; writing--review and editing, M.G.; visualization, J.S.; supervision, M.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Cultural and Education Grant Agency grant number KEGA: 020 TUKE-4/2022 and Science Grant Agency grant number VEGA: 1/0215/23 and VEGA: 1/0215/24.  
Not applicable.  
The original data presented in the study are available at [[http://www.sjf.tuke.sk/kr/images/download/Measred_data.xlsx](http://www.sjf.tuke.sk/kr/images/download/Measred_data.xlsx)]([http://www.sjf.tuke.sk/kr/images/download/Measred_data.xlsx](http://www.sjf.tuke.sk/kr/images/download/Measred_data.xlsx)).  
**Acknowledgments:** This research was supported by project KEGA: 020 TUKE-4/2022 Development and implementation of new approaches in teaching industrial and collaborative robotics and VEGA: 1/0215/23 Research and development of robotic workplaces equipped with industrial and collaborative robots and VEGA: 1/0294/24 Research and development of a multi-robotic system with distributed intelligence in the cloud.  
**Conflicts of Interest:** The authors declare no conflicts of interest.