Automated UAV to Survey and Monitor Ionising Radiation Levels in a Closed Environment

• Autorzy: Anand R. , Harshith Kumar M. B. , Raghavan Anirudh , Maddara Roopak , Anand Prajna

Identyfikatory

DOI

10.2478/pead-2022-0010

• Języki publikacji: EN

Abstrakty

EN

Since tragedies caused by nuclear disasters are always a concern, it is essential that nuclear power plants be monitored on a regular basis for any irregularities in ionising radiation levels. Irrespective of leakage proof measures being deployed in the plant, ensuring the safety of these measures is necessary. Given this scenario, the present study proposes the usage of unmanned aerial vehicles (UAVs) to ensure that radiation levels in nuclear plants remain within safe limits. The UAV deployed will map the entire environment following a unique path planning algorithm and monitor the environment with an onboard radiation sensor. If any irregularities are detected, the positional coordinates are flagged, and the A* algorithm is implemented to generate the shortest path between the starting point, and the flagged coordinates, which are considered as the destination coordinates. The UAV is made to traverse the shortest path together with maintaining stability of the system while traversing.

Słowa kluczowe

EN

unmanned aerial vehicle; indoor SLAM; sensor data; disaster management robot; path planning; obstacle avoidance

• Wydawca: De Gruyter
• Czasopismo: Power Electronics and Drives
• Rocznik: 2022
• Tom: Vol. 7 (42)
• Strony: 134--145
• Opis fizyczny: Bibliogr. 13 poz., rys.

Twórcy

autor

Anand R.

• Otis Elevator Company, Research and Development, Bangalore, India

• https://orcid.org/0000-0003-1226-2481

autor

Harshith Kumar M. B.

• Department of Electronics and Communication Engineering, PES University, Bangalore, India

autor

Raghavan Anirudh

• Department of Electronics and Communication Engineering, PES University, Bangalore, India

autor

Maddara Roopak

• Department of Electronics and Communication Engineering, PES University, Bangalore, India

autor

Anand Prajna

• School of Computer Science Engineering, RV University, Bangalore, India

Bibliografia

• Aleotti J, Micconi G, Caselli S, Benassi G, Zambelli N, Calestani D, Zanichelli M, Bettelli M, Zappettini A (2015). Unmanned Aerial Vehicle Equipped with Spectroscopic CdZnTe Detector for Detection and Identification of Radiological and Nuclear Material. In: 2015 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC), pp. 1–5. San Diego, CA, USA.
• Bhattacharyya, U. and Baum, C. (2018). Estimating the Location of a Nuclear Source in a Three-Dimensional Environment Using a Two-Stage Adaptive Algorithm. In: 2018 IEEE 8th Annual Computing and Communication Workshop and Conference (CCWC), pp. 8–14. Las Vegas, NV, USA.
• Cai, C., Carter, B., Srivastava, M., Tsung, J., Vahedi-Faridi, J. and Wiley, C. (2016). Designing a Radiation Sensing UAV System. In: 2016 IEEE Systems and Information Engineering Design Symposium (SIEDS), pp. 165–169. Charlottesville, VA, USA.
• Čerba, Š., Lüley, J., Vrban, B., Osuský, F. and Nečas, V. (2020). Unmanned Radiation-Monitoring System, IEEE Transactions on Nuclear Science, 67(4), pp. 636–643.
• Duck, A., Munasinghe, K. S. and Reakes, T. (2017). Gas and Radiation Sensor Array for Deployment on UAV, ROV and as a Handheld Standalone Device. In: 2017 Eleventh International Conference on Sensing Technology (ICST), pp. 1–5. Sydney, NSW, Australia.
• Hartman, J., Barzilov, A. and Novikov, I. (2015). Remote Sensing of Neutron and Gamma Radiation using Aerial Unmanned Autonomous System. In: 2015 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC), pp. 1–4. San Diego, CA, USA.
• Krátký, V., Petráček, P., Báča, T. and Saska, M. (2021). An Autonomous Unmanned Aerial Vehicle System for Fast Exploration of Large Complex Indoor Environments. Journal of Field Robotics, 38, pp. 1036–1058.
• Micconi, G., Aleotti, J. and Caselli, S. (2016). Evaluation of a Haptic Interface for UAV Teleoperation in Detection of Radiation Sources, 2016. In: 18th Mediterranean Electrotechnical Conference (MELECON), pp. 1–6. Lemesos, Cyprus.
• Morita, T., Oyama, K., Mikoshi, T. and Nishizono, T. (2018). Decision Making Support of UAV Path Planning for Efficient Sensing in Radiation Dose Mapping. In: 2018 IEEE 42nd Annual Computer Software and Applications Conference (COMPSAC), pp. 333–338. Milwaukee, WI, USA.
• Pani, R., Camera, F., Pergola, A., Polito, C., Falconi, R., Franciosini, G., Longo, M., Bettiol, M., Frantellizzi, V., de Vincentis, G., & Pani, A (2019). Novel Gamma Tracker for Rapid Radiation Direction Detection for UAV Drone Use. In: 2019 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC), pp. 1–3. Manchester, UK.
• Reyes Munoz, A., Pastor Llorens, E., Barrado Muxi, C. and Gasull, M. (2015). Monitoring Radiological Incidents through an Opportunistic Network. IEEE Latin America Transactions, 13(1), pp. 54–61.
• Rudolph, C., Knoedler, B. and Heinskill, J. (2020). Comparable Data Evaluation Method for a Radio-Nuclear Sensor When Used on an UAV. In: 2020 IEEE SENSORS, pp. 1–4. Rotterdam, Netherlands.
• Seo, J.W., Han, S. H. and Young Shin, S. (2021). Deep Learning Based Nuclear Power Plant Monitoring System using UAV. In: 2021 International Conference on Electronics, Information, and Communication (ICEIC), pp. 1–4. Jeju, Korea (South).