A SYSTEM FOR DETECTING EXPLOSIVE OBJECTS BASED ON A MOBILE TRACKED ROBOT CONTROLLED FROM A QUADCOPTER USING MACHINE VISION
DOI:
https://doi.org/10.17721/AIT.2025.1.04Keywords:
explosive objects, Arduino, pulse metal detector, tracked mobile robot, quadcopter, computer vision, autonomous control, metal detection.Abstract
Background. The results presented in this article mark the beginning of the project team’s research in this direction. The experiments were conducted under laboratory conditions and focus on the general concept and identification of directions for further development. Future research is planned to be conducted in real or near-real conditions. The work proposes a system for detecting explosive objects based on the K158 pulse metal detector, a microcontroller, and the Keyestudio Mini Tank V3.0 mobile tracked platform. An integration with a quadcopter equipped with computer vision is proposed, enabling precise targeting and remote control. The system architecture, component interaction principles, decision-making logic, and a mathematical model of operation are described.
Methods. The research employed methods of functional subsystem modeling, computer-based oscillographic analysis of electrical signals, mathematical modeling of the tracked robot’s kinematics in XY coordinates, and algorithmic design of a control automaton. To test the interaction with the quadcopter, virtual testing was conducted in a simulation environment using machine vision. Signal processing was implemented through microcontroller programming based on threshold filtering.
Results. The results demonstrate a clear differentiation between signals in the presence and absence of metal. In the background mode, a stable zero line is observed, while detection of a target produces a series of pulses at a frequency of 125–150 Hz and a current of up to 625 mA. This ensures reliable real-time object identification. All components, including the power and wireless communication modules, showed stable operation. Machine vision-based targeting achieved an accuracy of ±20 cm. Overall, the system demonstrated reliability and efficiency in laboratory conditions.
Conclusions. The proposed system for detecting explosive objects combines the advantages of analog sensing, digital signal processing, a mobile platform, and aerial coordination. Its simplicity, autonomy, and low cost make it suitable for use in high-risk areas.
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References
Abdulmajeed, W. R., & Hussein, M. A. (2015). Factors effect on metal detecting system using mobile robot. International Journal of Computer Applications, 126(9), 43–46. https://doi.org/10.5120/ijca2015906194
Jeyagopi, R., Chan, C., & Ma’arof, M. (2022). Design metal detecting Arduino remote control robot vehicle controlled via Bluetooth. Journal of Innovation and Technology, 2022. https://iuojs.intimal.edu.my/index.php/joit/article/view/287
Kuzavkov, V. (2022). Application of methods of technical diagnosis in solving problems of cybernetic protection. Ukrainian Information Security Research Journal, 24(1), 29–36 [in Ukrainian]. https://doi.org/10.18372/2410-7840.24.16861
Mishchuk, V. V., & Fesenko, H. V. (2024). Analysis of Computer Vision Methods and Means for Explosive Ordnance Detection Mobile Systems. Èlektronnoe modelirovanie, 46(1), 90–111 [in Ukrainian]. https://doi.org/10.15407/emodel.46.01.090
Mujiarto, B. P., Sambas, A., & Haerudin, I. (2021). Design of Arduino-based metal detector robot. ResearchGate. https://www.researchgate.net/publication/355840656_Design_of_Arduino-Based_Metal_Detector_Robot
Nevlyudov, I., Yanushkevich, D., Tolkunov, I., Popov, I., & Ivanets, H. (2023). Justification of the need to create modern robotic and technical complexes for humanitarian demining. Problems of Emergency Situations, 2(38), 17–38 [in Ukrainian]. https://doi.org/10.52363/2524-0226-2023-38-2
Oleksenko, O., Misiuk, H., IkaіevD., Korshok, V., & Palka, V. (2024). MAIN TRENDS IN THE USE OF UNMANNED AIRCRAFT IN THE RUSSIAN-UKRAINIAN WAR. Scientific works
https://doi.org/10.37701/dndivsovt.21.2024.12
Ravi Kiran, B., Padmini Rani, S., Venkatesh, S., Chandra Kanth, Y., Sasank Sai Krishna, M., & Siva Prasad, B. (2024). Metal detector robot using surveillance camera. International Journal of Research Publication and Reviews, 5(5). https://ijrpr.com/uploads/V5ISSUE5/IJRPR27963.pdf
Reshetnyak, M. (2024). Development of a metal detector automation system using Arduino (Explanatory note to the certification work of a higher education applicant at the first (bachelor's) level). Ministry of Education and Science of Ukraine; Kharkiv. National University of Radio Electronics [in Ukrainian]. https://openarchive.nure.ua/handle/document/27821
Savin, V. (2024). Improvement of the method for detecting explosive objects: Qualification work. OpenArchive NURE. Kharkiv National University of Radio Electronics. [in Ukrainian]. https://openarchive.nure.ua/handle/document/25976
Shovkoshytnyi , I. ., & Vasylenko, O. . (2024). Selection of indicators for assessing the effectiveness of the use of swarms of striking unmanned aerial vehicles to defeat non-stationary group targets. Air Power of Ukraine, 2(7), 61–72 [in Ukrainian]. https://doi.org/10.33099/2786-7714-2024-2-7-61-72
Solodchuk, M., & Voitenko, V. (2022). Selection of an onboard object detector for UAVs. In Development and Modernization of Military Equipment for the Needs of the Armed Forces of Ukraine (pp. 280–282). Lund University. [in Ukrainian]
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