## 6 Conclusions  
This study presented the design and implementation of a real-time collision avoidance system for indoor drones using multiple Time-of-Flight (ToF) distance sensors. The proposed system enables 360-degree horizontal obstacle detection and automatic flight command modification without fully overriding manual control. A total of eight ToF distance sensors were integrated into a custom-designed sensor module and mounted on the drone to provide full directional coverage.  
The core of the system is a Gaussian-based repulsive force model, which transforms the measured distances from each sensor into virtual force vectors. These forces are averaged and used to modify the SBUS control signals in real time, ensuring safe navigation without disrupting operator intent. The drone only overrides user commands when a potential collision is detected, maintaining responsive and intuitive manual control.  
The entire system was implemented on a low-cost microcontroller, demonstrating that high-performance real-time obstacle avoidance can be achieved without relying on high computational resources. The algorithm was validated through a series of indoor flight experiments in a cluttered environment. Results showed that the system effectively prevented collisions by modifying control commands proportionally based on obstacle proximity.  
To further evaluate the system's behavior, both real-time visualizations using RViz and trajectory tracking using the OptiTrack motion capture system were conducted. The results confirmed that the drone successfully adjusted its path in response to obstacle detection, validating the effectiveness of the repulsive force model and control modification algorithm. Since the developed system does not include onboard localization or mapping, the OptiTrack system provided essential ground-truth data for analyzing drone motion and verifying performance.  
This research demonstrates a practical, low-cost solution for enhancing indoor drone safety during manual operations, especially in environments where GPS is unavailable and sensor-based navigation is required. The modular nature of the system also allows for future upgrades, including integration with onboard mapping, SLAM, or autonomous waypoint navigation systems.  
Future enhancements may focus on expanding sensor coverage and optimizing placement to detect smaller and more distant obstacles. Developing sophisticated algorithms, possibly incorporating machine learning and AI techniques, could further improve the drone's navigation and collision avoidance capabilities [37]. Additionally, refining the positioning of sensors on the drone body can enhance measurement accuracy and system efficiency, reduce blind spots, and ensure consistent performance.  
Conceptualization, J.H.L.; methodology, T.A.G. and J.H.L.; software, T.A.G.; validation, T.A.G. and J.H.L.; formal analysis, T.A.G. and J.H.L.; investigation, T.A.G. and J.H.L.; resources, J.H.L.; data curation, T.A.G.; writing--original draft preparation, T.A.G.; writing--review and editing, J.H.L.; visualization, T.A.G.; supervision, J.H.L.; project administration, J.H.L.; funding acquisition, J.H.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.
Data Availability Statement: The original contributions presented in this study are included in the article. Further inquiries can be directed at the author.  
Conflicts of Interest: The authors declare no conflicts of interest regarding the publication of this paper. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.