Journal of Electrical Engineering and Automation
IRO Journals
Volume 6 — Issue 4 — December 2024

RoboSpy: Autonomous Night Vision Surveillance Robot with Spying Camera for War Field Operations

https://doi.org/10.36548/jeea.2024.4.004
Jeffrey Sham R S.
Department of Electronics & Instrumentation Engineering, Dayananda Sagar College of Engineering Bangalore, India
Yashwanth M G.
Department of Electronics & Instrumentation Engineering, Dayananda Sagar College of Engineering Bangalore, India
Vamshi Krishna Y
Department of Electronics & Instrumentation Engineering, Dayananda Sagar College of Engineering Bangalore, India
PDF
PDF

How to Cite

S., Jeffrey Sham R, Yashwanth M G., and Vamshi Krishna Y. 2025. “RoboSpy: Autonomous Night Vision Surveillance Robot With Spying Camera for War Field Operations”. Journal of Electrical Engineering and Automation 6 (4): 315-24. https://doi.org/10.36548/jeea.2024.4.004.
ACM ACS APA ABNT Chicago Harvard IEEE MLA Turabian Vancouver AMA

Download Citation

Endnote/Zotero/Mendeley (RIS) BibTeX

Keywords

— Autonomous Robot
— Night Vision
— HAAR Cascade
— Remote Monitoring
Published: 12-02-2025

Abstract

RoboSpy is an autonomous surveillance robot designed for war field operations, equipped with night vision capabilities and a spying camera. Its primary goal is to provide real-time intelligence, enhance situational awareness, and reduce risks to soldiers by conducting reconnaissance in hazardous conditions. RoboSpy integrates advanced sensors, hardware, and software to navigate complex environments, detect obstacles, and transmit critical data to remote operators. The system architecture features a Raspberry Pi as the processing unit, ultrasonic sensors to detect objects, and a high-resolution night vision camera for real-time video capture. The software utilizes an intelligent HAAR Cascade classifier algorithm for object detection, path planning algorithms for navigation, and enhanced image processing techniques to capture the moving image. The RoboSpy provides real-time support for security forces to monitor the movement of intruders in the restricted area. This research discusses the design, implementation, and potential military applications of RoboSpy, emphasizing its role in reducing human intervention during high-risk reconnaissance missions.

References

  1. Kanagaraj, R., Amsaveni, M.M., Binsha, S., Chella Keerthana, S.: Raspberry Pi-Based Spy Robot with Facial Recognition. In: Pandian, A.P., Palanisamy, R., Narayanan, M., and Senjyu, T. (eds.) Proceedings of Third International Conference on Intelligent Computing, Information and Control Systems. Springer Nature Singapore, Singapore (2022). 29–40.
  2. Jayakumar, A.J.K., Muthulakshmi, S.: Raspberry Pi-Based Surveillance System with IoT. In: Thalmann, D., Subhashini, N., Mohanaprasad, K., and Murugan, M.S.B. (eds.) Intelligent Embedded Systems. Springer Singapore, Singapore (2018). 173–185.
  3. Chang, Hsing-Cheng, Yu-Liang Hsu, Ching-Yuan Hsiao, and Yi-Fan Chen. "Design and implementation of an intelligent autonomous surveillance system for indoor environments." IEEE Sensors Journal 21, no. 15 (2021): 17335-17349.
  4. Witwicki, Stefan, Jose Carlos Castillo, Joao Messias, Jesus Capitan, Francisco S. Melo, Pedro U. Lima, and Manuela Veloso. "Autonomous surveillance robots: A decision-making framework for networked muiltiagent systems." IEEE Robotics & Automation Magazine 24, no. 3 (2017): 52-64.
  5. Zhao, Yanfei, Peng Xiao, Jingchuan Wang, and Rui Guo. "Semi-Autonomous Navigation Based on Local Semantic Map for Mobile Robot." Journal of Shanghai Jiaotong University (Science) (2023): 1-7.
  6. Sushma, R., and J. Satheesh Kumar. "Dynamic vehicle modelling and controlling techniques for autonomous vehicle systems." Journal of Electrical Engineering and Automation 4, no. 4 (2023): 307-315.
  7. Sushma, R., and J. Satheesh Kumar. "A survey on autonomous vehicles-benefits and drawbacks." Journal of Electronics and Informatics 4, no. 4 (2023): 237-245.
  8. Jishma, C. P., B. V. Praharshitha, Arpita Laxmi, S. K. Anusha, and J. Satheesh Kumar. "Development of Intelligent Driver Assistant System for Cognitive Cars." Journal of IoT in Social, Mobile, Analytics, and Cloud 4, no. 2 (2022): 108-118.
  9. Farhood, Helia, Ivan Bakhshayeshi, Matineh Pooshideh, Nabi Rezvani, and Amin Beheshti. "Recent advances of image processing techniques in agriculture." Artificial Intelligence and Data Science in Environmental Sensing (2022): 129-153.
  10. Youssef, Almoatazbellah, Andreas Rosenwald, and Mathias Tillmann Rosenfeldt. "TelePi: an affordable telepathology microscope camera system anyone can build and use." Virchows Archiv 485, no. 1 (2024): 115-122.
  11. Gaur, Sachin, Milind Pandey, and Himanshu. "Realization of Facial Recognition Technology for Attendance Monitoring Through Biometric Modalities Employing MTCNN Integration." SN Computer Science 5, no. 7 (2024): 862.
  12. Wang, Y., Wang, H.: A face template: Improving the face generation quality of multi-stage generative adversarial networks using coarse-grained facial priors. Multimed Tools Appl. 83, (2024). 21677–21693.
  13. Shetty, Anirudha B., and Jeevan Rebeiro. "Facial recognition using Haar cascade and LBP classifiers." Global Transitions Proceedings 2, no. 2 (2021): 330-335.
  14. Obukhov, A.: Haar classifiers for object detection with CUDA. GPU Computing Gems Emerald Edition. 517–544 (2011).
  15. Tasyurek, Murat, and Ertugrul Gul. "A new deep learning approach based on grayscale conversion and DWT for object detection on adversarial attacked images." The Journal of Supercomputing 79, no. 18 (2023): 20383-20416.
  16. Abirami, S.P., Kousalya, G., Balakrishnan, Karthick, R.: Varied Expression Analysis of Children With ASD Using Multimodal Deep Learning Technique. Deep Learning and Parallel Computing Environment for Bioengineering Systems. 225–243 (2019).
  17. Culjak, I., Abram, D., Pribanic, T., Dzapo, H., Cifrek, M.: A brief introduction to OpenCV. In: 2012 Proceedings of the 35th International Convention MIPRO. (2012). 1725–1730.