International Journal of Emerging Research in Science, Engineering, and Management
Vol. 2, Issue 3, pp. 118-126, March 2026.
This work is licensed under a Creative Commons Attribution 4.0 International License.
Real-Time Bomb Sensing Robotic Platform with Wireless Video Surveillance
A J Reuben Thomas Raj
B Mahesh
B Nagendra Babu
B Lokeswara Rao
S Manoj Kumar
C Mohan Reddy
Department of ECE, Siddhartha Institute of Science and Technology, Puttur, AP, India.
Abstract: Bomb detection and surveillance in hazardous environments require intelligent robotic systems that reduce direct human involvement and improve operational safety. This paper presents the design and implementation of an IoT-based real-time bomb sensing robotic platform integrated with wireless video surveillance using an ESP32 microcontroller. The proposed system incorporates a metal detector for identifying metallic explosive components, an LPG gas sensor for detecting hazardous gases, and a DHT11 sensor for monitoring environmental conditions such as temperature and humidity. A wireless night vision camera enables real-time video streaming for remote monitoring through a mobile-based interface. The robotic platform also includes a motor driver-based navigation system and a robotic arm mechanism for safe handling of suspicious objects. Experimental results demonstrate reliable sensor performance, effective wireless communication, and stable robotic movement during testing. The proposed system provides a cost-effective and efficient solution for surveillance, hazardous environment monitoring, and defence-related applications.
Keywords: Bomb Detection, IOT-Based Robotics, ESP32, Metal Detector, Gas sensor.
References:
- O. O. Tooki, A. A. Aderinto, and O. M. Popoola, “Development of an intelligent-based telemetry hexapod robotic system for surveillance of power system components,” e-Prime – Advances in Electrical Engineering Electronics and Energy, vol. 10, p. 100806, Oct. 2024, doi: 10.1016/j.prime.2024.100806.
- M. Tamilselvi, T. Manimegalai, G. Ramkumar, S. A. Shifani, and V. Mohanavel, “Internet of Things enabled Energy-Efficient flying robots for agricultural field monitoring using smart sensors,” in Apple Academic Press eBooks, 2023, pp. 59–73. doi: 10.1201/9781003314851-7.
- A. B and S. D, “An IoT Based Multi functional Robot for Military Applications,” www.ijrdo.org, Apr. 2019, doi: 10.53555/eee.v5i3.2782.
- M. Obosu and S. Frimpong, “Advances in automation and robotics: The state of the emerging future mining industry,” Journal of Safety and Sustainability, vol. 2, no. 3, pp. 181–194, May 2025, doi: 10.1016/j.jsasus.2025.05.003.
- J. Azeta et al., “An Android based mobile robot for monitoring and surveillance,” Procedia Manufacturing, vol. 35, pp. 1129–1134, Jan. 2019, doi: 10.1016/j.promfg.2019.06.066.
- I. Ullah, D. Adhikari, H. Khan, M. S. Anwar, S. Ahmad, and X. Bai, “Mobile robot localization: Current challenges and future prospective,” Computer Science Review, vol. 53, p. 100651, Jul. 2024, doi: 10.1016/j.cosrev.2024.100651.
- J.-P. A. Yaacoub, H. N. Noura, and B. Piranda, “The internet of modular robotic things: Issues, limitations, challenges, & solutions,” Internet of Things, vol. 23, p. 100886, Jul. 2023, doi: 10.1016/j.iot.2023.100886.
- S. Nahavandi, R. Alizadehsani, D. Nahavandi, C. P. Lim, K. Kelly, and F. Bello, “Machine learning meets advanced robotic manipulation,” Information Fusion, vol. 105, p. 102221, Jan. 2024, doi: 10.1016/j.inffus.2023.102221.
- P. Ben-Tzvi, A. A. Goldenberg, and J. W. Zu, “Articulated hybrid mobile robot mechanism with compounded mobility and manipulation and on-board wireless sensor/actuator control interfaces,” Mechatronics, vol. 20, no. 6, pp. 627–639, Jul. 2010, doi: 10.1016/j.mechatronics.2010.06.004.
- M. U. Farooq, A. Eizad, and H.-K. Bae, “Power solutions for autonomous mobile robots: A survey,” Robotics and Autonomous Systems, vol. 159, p. 104285, Oct. 2022, doi: 10.1016/j.robot.2022.104285.
- J. Li, H. Godaba, Z. Q. Zhang, C. C. Foo, and J. Zhu, “A soft active origami robot,” Extreme Mechanics Letters, vol. 24, pp. 30–37, Aug. 2018, doi: 10.1016/j.eml.2018.08.004.
- Y. Liu, W. Zhang, S. Pan, Y. Li, and Y. Chen, “Analyzing the robotic behavior in a smart city with deep enforcement and imitation learning using IoRT,” Computer Communications, vol. 150, pp. 346–356, Nov. 2019, doi: 10.1016/j.comcom.2019.11.031.
- Y. Tan and Z.-Y. Zheng, “Research advance in swarm robotics,” Defence Technology, vol. 9, no. 1, pp. 18–39, Mar. 2013, doi: 10.1016/j.dt.2013.03.001.
- S. Cebollada, L. Payá, M. Flores, A. Peidró, and O. Reinoso, “A state-of-the-art review on mobile robotics tasks using artificial intelligence and visual data,” Expert Systems With Applications, vol. 167, p. 114195, Nov. 2020, doi: 10.1016/j.eswa.2020.114195.
