Autonomous Surveillance Robot for Enhanced Security

  • Zubaidah Abdul Rahman 
    Politeknik Kuala Terengganu
    Malaysia
  • Sharifah Nurulhuda Tuan Mohd Yasin
    Politeknik Kuala Terengganu
    Malaysia
  • Muhammad Farish Idlan Mohd Asrol
    Politeknik Kuala Terengganu
    Malaysia

Abstract

The need for advanced surveillance systems has increased in recent years, reflecting the growing need for increased security measures in a variety of areas, including public safety, industrial monitoring, and military activities. This abstract proposes the development and execution of an autonomous surveillance robot equipped with cutting-edge technologies to improve security monitoring capabilities. By integrating a variety of sensors, such as motion detectors, infrared sensors, and cameras, the suggested surveillance robot is able to precisely and accurately sense its surroundings. With the use of artificial intelligence algorithms, the robot can detect and monitor several targets, navigate through complex areas on its own, and recognize suspicious activity in real time. One of the surveillance robot's primary characteristics is its strong mobility, which enables it to move across a variety of surfaces, including both indoor and outdoor ones. In addition, the robot is built to function well in a range of weather scenarios, guaranteeing continuous observation capabilities. All things considered, the suggested autonomous surveillance robot is a noteworthy development in security technology, providing better situational awareness, faster reaction times, and increased surveillance capabilities, all of which eventually raise the general level of safety and security in both public and private areas.
References
  1. Ahmed, M., & Omar, H. (2022). Design considerations for autonomous security robots: Energy and cost challenges. Journal of Robotics and Automation, 12(2), 110–119. https://doi.org/10.1016/j.robot.2022.04.005
  2. Bimbraw, S. (2015). Autonomous robots: A survey of control and navigation techniques. Springer.
  3. Calo, R. (2011). The drone as privacy catalyst. Stanford Law Review Online, 64, 29-33.
  4. Chen, M., & Lin, Y. (2014). Edge computing for autonomous systems: Architectures and applications. IEEE Network, 28(3), 16-23.
  5. Chung, W., Kim, S., & Lee, H. (2020). Integrating autonomous robots into existing security systems: A systems engineering approach. International Journal of Smart Security Technologies, 6(1), 35–50. https://doi.org/10.4018/IJSST.2020010103
  6. Finn, R. L., & Wright, D. (2012). Unmanned aircraft systems: Surveillance, ethics and privacy in civil applications. Computer Law & Security Review, 28(2), 184-194.
  7. Floreano, D., & Wood, R. J. (2015). Science, technology and the future of small autonomous drones. Nature, 521(7553), 460-466.
  8. Giesecke, F. E., Mitchell, A., Spencer, H. C., Hill, I. L., Dygdon, J. T., & Novak, J. E. (2012). *Technical drawing*. Pearson.
  9. Gonzalez, J. A., Chen, Y., & Patel, R. (2020). Surveillance robotics: Enhancing perimeter security with autonomous patrolling. Robotics and Autonomous Systems, 131, 103585. https://doi.org/10.1016/j.robot.2020.103585
  10. Hany, Y., Kelg, Y., Emam, Y., Bendary, N., Abdelshafy, Y., Alkady, G. I., & Mekhail, M. (2024). YOLOv8-Based Surveillance Robot for Real-Time Threat Assessment and Mitigation. 577–580. https://doi.org/10.1109/niles63360.2024.10753171
  11. Hartley, R., & Zisserman, A. (2003). *Multiple view geometry in computer vision*. Cambridge University Press.
  12. IEEE Standards Association. (2017). IEEE Standard for Ethical Design Process. IEEE Std 2857-2021.
  13. Jones, J. L., & Flynn, A. M. (2015). *Mobile robots: Inspiration to implementation*. CRC Press.
  14. Kim, D., & Park, J. (2021). Mobility and resilience of autonomous surveillance robots in complex environments. Sensors, 21(5), 1687. https://doi.org/10.3390/s21051687
  15. Kumar, P., Soni, P., & Gupta, R. (2021). Artificial intelligence in surveillance robots: A comprehensive review. International Journal of Robotics and Automation, 36(2), 167-185. https://doi.org/10.1016/j.ijra.2021.04.001
  16. Lee, D., Kim, H., & Park, Y. (2020). Machine learning techniques for autonomous surveillance robots: A review. Journal of Robotics and Automation, 8(3), 222-240. https://doi.org/10.1002/jra.123
  17. Murphy, R. R., Tadokoro, S., & Kleiner, A. (2016). Disaster robotics. In Springer handbook of robotics (pp. 1577-1604). Springer.
  18. Purkaystha, D., Islam, M. U., Gupta, A. D., Saha, S., & Bhuyan, M. H. (2024). Advanced autonomous surveillance robot for enhanced monitoring and individual identification. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 15(3), 25–34. https://doi.org/10.61841/turcomat.v15i3.14722
  19. Rubenstein, M., Cornejo, A., & Nagpal, R. (2014). Programmable self-assembly in a thousand-robot swarm. Science, 345(6198), 795-799.
  20. Rubenstein, M., Cornejo, A., & Nagpal, R. (2014). Programmable self-assembly in a thousand-robot swarm. Science, 345(6198), 795-799.
  21. Russell, S., & Norvig, P. (2021). Artificial intelligence: A modern approach (4th ed.). Pearson.
  22. Schneier, B. (2015). Data and Goliath: The hidden battles to collect your data and control your world. W. W. Norton & Company.
  23. Siciliano, B., & Khatib, O. (Eds.). (2016). Springer handbook of robotics. Springer.
  24. Siciliano, B., & Khatib, O. (Eds.). (2016). Springer handbook of robotics (2nd ed.). Springer.
  25. Siegwart, R., Nourbakhsh, I. R., & Scaramuzza, D. (2011). *Introduction to autonomous mobile robots*. MIT Press.
  26. Thrun, S., Burgard, W., & Fox, D. (2005). Probabilistic robotics. MIT Press.
  27. Ullman, D. G. (2010). *The mechanical design process*. McGraw-Hill.
  28. Zhou, Q., & Zhang, L. (2018). Sensor fusion for autonomous surveillance systems. Sensors, 18(9), 3002. https://doi.org/10.3390/s18093002

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