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JRM Vol.35 No.4 pp. 1084-1091
doi: 10.20965/jrm.2023.p1084
(2023)

Paper:

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Autonomous Navigation System for Multi-Quadrotor Coordination and Human Detection in Search and Rescue

Jeane Marina Dsouza* ORCID Icon, Rayyan Muhammad Rafikh*,** ORCID Icon, and Vishnu G. Nair***,† ORCID Icon

*Department of Mechatronics, Manipal Institute of Technology, Manipal Academy of Higher Education
Manipal, Karnataka 576104, India

**Electrical and Computer Engineering Department, Sultan Qaboos University
Al Khawd, Muscat 123, Oman

***Department of Aeronautical and Automobile Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education
Manipal, Karnataka 576104, India

†Corresponding author

Received:
November 3, 2022
Accepted:
May 18, 2023
Published:
August 20, 2023
Creative Commons License
Keywords:
disaster management, convolutional neural network, deep learning, multi-robot coordination, region partitioning
Abstract

There are many methodologies assisting in the detection and tracking of trapped victims in the context of disaster management. Disaster management in the aftermath of such sudden occurrences requires preparedness in terms of technology, availability, accessibility, perception, training, evaluation, and deployability. This can be achieved through intensive test, evaluation and comparison of different techniques that are alternative to each other, eventually covering each module of the technology used for the search and rescue operation. Intensive research and development by academia and industry have led to an increased robustness of deep learning techniques such as the use of convolutional neural networks, which has resulted in increased reliance of first responders on the unmanned aerial vehicle (UAV) technology equipped with state-of-the-art computers to process real-time sensory information from cameras and other sensors in quest of possibility of life. In this paper, we propose a method to implement simulated detection of life in the sudden onset of disasters with the help of a deep learning model, and simultaneously implement multi-robot coordination between the vehicles with the use of a suitable region-partitioning technique to further expedite the operation. A simulated test platform was developed with parameters resembling real-life disaster environments using the same sensors.

UAV deployment and uncertainty profile

UAV deployment and uncertainty profile

Cite this article as:
J. Dsouza, R. Rafikh, and V. Nair, “Autonomous Navigation System for Multi-Quadrotor Coordination and Human Detection in Search and Rescue,” J. Robot. Mechatron., Vol.35 No.4, pp. 1084-1091, 2023.
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References
  1. [1] A. Restas, “Drone applications for supporting disaster management,” World J. Eng. Technol., Vol.3, No.3C, pp. 316-321, 2015. https://doi.org/10.4236/wjet.2015.33c047
  2. [2] A. Bucknell and T. Bassindale, “An investigation into the effect of surveillance drones on textile evidence at crime scenes,” Sci. Justice, Vol.57, No.5, pp. 373-375, 2017. https://doi.org/10.1016/j.scijus.2017.05.004
  3. [3] B. Bansod, R. Singh, R. Thakur, and G. Singhal, “A comparison between satellite based and drone based remote sensing technology to achieve sustainable development: A review,” J. Agric. Environ. Int. Dev., Vol.111, No.2, pp. 383-407, 2017. https://doi.org/10.12895/jaeid.20172.690
  4. [4] R. Ladig, H. Paul, R. Miyazaki, and K. Shimonomura, “Aerial manipulation using multirotor UAV: A review from the aspect of operating space and force,” J. Robot. Mechatron., Vol.33, No.2, pp. 196-204, 2021. https://doi.org/10.20965/jrm.2021.p0196
  5. [5] H. Darweesh, E. Takeuchi, K. Takeda, Y. Ninomiya, A. Sujiwo, L. Y. Morales, N. Akai, T. Tomizawa, and S. Kato, “Open source integrated planner for autonomous navigation in highly dynamic environments,” J. Robot. Mechatron., Vol.29, No.4, pp. 668-684, 2017. https://doi.org/10.20965/jrm.2017.p0668
  6. [6] K. Nonami, “Drone technology, cutting-edge drone business, and future prospects,” J. Robot. Mechatron., Vol.28, No.3, pp. 262-272, 2016. https://doi.org/10.20965/jrm.2016.p0262
  7. [7] K. Nonami, “Research and development of drone and roadmap to evolution,” J. Robot. Mechatron., Vol.30, No.3, pp. 322-336, 2018. https://doi.org/10.20965/jrm.2018.p0322
  8. [8] T. Maki, Y. Noguchi, Y. Kuranaga, K. Masuda, T. Sakamaki, M. Humblet, and Y. Furushima, “Low-altitude and high-speed terrain tracking method for lightweight AUVs,” J. Robot. Mechatron., Vol.30, No.6, pp. 971-979, 2018. https://doi.org/10.20965/jrm.2018.p0971
  9. [9] D. Iwakura and K. Nonami, “Indoor localization of flying robot by means of infrared sensors,” J. Robot. Mechatron., Vol.25, No.1, pp. 201-210, 2013. https://doi.org/10.20965/jrm.2013.p0201
  10. [10] U. E. Franke, “Civilian drones: Fixing an image problem?,” ISN Blog. Int. Relations and Security Network, Retrieved, Vol.5, 2015.
  11. [11] P. Chun, J. Dang, S. Hamasaki, R. Yajima, T. Kameda, H. Wada, T. Yamane, S. Izumi, and K. Nagatani, “Utilization of unmanned aerial vehicle, artificial intelligence, and remote measurement technology for bridge inspections,” J. Robot. Mechatron., Vol.32, No.6, pp. 1244-1258, 2020. https://doi.org/10.20965/jrm.2020.p1244
  12. [12] K. Hidaka, D. Fujimoto, and K. Sato, “Autonomous adaptive flight control of a UAV for practical bridge inspection using multiple-camera image coupling method,” J. Robot. Mechatron., Vol.31, No.6, pp. 845-854, 2019. https://doi.org/10.20965/jrm.2019.p0845
  13. [13] S. Hayat, E. Yanmaz, and R. Muzaffar, “Survey on unmanned aerial vehicle networks for civil applications: A communications viewpoint,” IEEE Commun. Surv. Tutor., Vol.18, No.4, pp. 2624-2661, 2016. https://doi.org/10.1109/COMST.2016.2560343
  14. [14] A. Giyenko and Y. I. Cho, “Intelligent UAV in smart cities using IoT,” 2016 16th Int. Conf. Control Autom. Syst. (ICCAS), pp. 207-210, 2016. https://doi.org/10.1109/ICCAS.2016.7832322
  15. [15] P. Bupe, R. Haddad, and F. Rios-Gutierrez, “Relief and emergency communication network based on an autonomous decentralized UAV clustering network,” SoutheastCon 2015, 2015. https://doi.org/10.1109/SECON.2015.7133027
  16. [16] A. Giagkos, M. S. Wilson, E. Tuci, and P. B. Charlesworth, “Comparing approaches for coordination of autonomous communications UAVs,” 2016 Int. Conf. Unmanned Aircr. Syst. (ICUAS), pp. 1131-1139, 2016. https://doi.org/10.1109/ICUAS.2016.7502551
  17. [17] C. Luo, J. Nightingale, E. Asemota, and C. Grecos, “A UAV-cloud system for disaster sensing applications,” 2015 IEEE 81st Veh. Technol. Conf. (VTC Spring), 2015. https://doi.org/10.1109/VTCSpring.2015.7145656
  18. [18] J. Scherer and B. Rinner, “Persistent multi-UAV surveillance with energy and communication constraints,” 2016 IEEE Int. Conf. Autom. Sci. Eng. (CASE), pp. 1225-1230, 2016. https://doi.org/10.1109/COASE.2016.7743546
  19. [19] C. Cai, B. Carter, M. Srivastava, J. Tsung, J. Vahedi-Faridi, and C. Wiley, “Designing a radiation sensing UAV system,” 2016 IEEE Syst. Inf. Eng. Des. Symp. (SIEDS), pp. 165-169, 2016. https://doi.org/10.1109/SIEDS.2016.7489292
  20. [20] Y. Liu, R. Lv, X. Guan, and J. Zeng, “Path planning for unmanned aerial vehicle under geo-fencing and minimum safe separation constraints,” 2016 12th World Congr. Intell. Control Autom. (WCICA), pp. 28-31, 2016. https://doi.org/10.1109/WCICA.2016.7578482
  21. [21] P. B. Sujit and R. Beard, “Multiple UAV path planning using anytime algorithms,” 2009 Am. Control Conf., pp. 2978-2983, 2009. https://doi.org/10.1109/ACC.2009.5160222
  22. [22] Y. Tanabe, M. Sugiura, T. Aoyama, H. Sugawara, S. Sunada, K. Yonezawa, and H. Tokutake, “Multiple rotors hovering near an upper or a side wall,” J. Robot. Mechatron., Vol.30, No.3, pp. 344-353, 2018. https://doi.org/10.20965/jrm.2018.p0344
  23. [23] D. Hollenbeck and Y. Chen, “Multi-UAV method for continuous source rate estimation of fugitive gas emissions from a point source,” 2021 Int. Conf. Unmanned Aircr. Syst. (ICUAS), pp. 1308-1313, 2021. https://doi.org/10.1109/ICUAS51884.2021.9476728
  24. [24] Y. Yaguchi and T. Tomeba, “Region coverage flight path planning using multiple UAVs to monitor the huge areas,” 2021 Int. Conf. Unmanned Aircr. Syst. (ICUAS), pp. 1677-1682, 2021. https://doi.org/10.1109/ICUAS51884.2021.9476775
  25. [25] U. Handalage and L. Kuganandamurthy, “Real-time object detection using YOLO: A review,” 2021. https://doi.org/10.13140/RG.2.2.24367.66723
  26. [26] T. Ahmad et al., “Object detection through modified YOLO neural network,” Sci. Program., Vol.2020, Article No.8403262, 2020. https://doi.org/10.1155/2020/8403262
  27. [27] R. Mendonça et al., “A cooperative multi-robot team for the surveillance of shipwreck survivors at sea,” OCEANS 2016 MTS/IEEE Monterey, 2016. https://doi.org/10.1109/OCEANS.2016.7761074
  28. [28] L. Wu, M. A. García García, D. Puig Valls, and A. Solé Ribalta, “Voronoi-based space partitioning for coordinated multi-robot exploration,” J. Pys. Agents, Vol.1, No.1, pp. 37-44, 2007. https://doi.org/10.14198/JoPha.2007.1.1.05
  29. [29] S. Hoshino and K. Takahashi, “Dynamic partitioning strategies for multi-robot patrolling systems,” J. Robot. Mechatron., Vol.31, No.4, pp. 535-545, 2019. https://doi.org/10.20965/jrm.2019.p0535

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