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UAV path planning method for avoiding restricted areas

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Abstract

Recently, the industry of drone systems has come into the spotlight because a new potential market has been revealed. A considerable number of drones are deployed worldwide as they can be used in many applications, providing a broad range of services, such as monitoring the surrounding environment, delivering services, in farming, and in rescue activities from disasters to accidents. This expansion is fostering the development of a comprehensive approach, including the construction of general systems, such as cyber-physical systems and IoT middleware platforms. In terms of the quantitative aspects of the drone industry, we still have many issues to solve and improve, such as privacy protection, human safety, improvement in resources, and specifically, power consumption and efficiency. To overcome these problems, systems must be able to generate an efficient and easy-to-follow path that is able to dynamically adjust to new situations. Thus, we propose an ONLINE/OFFLINE path planning algorithm and evaluate the results of a simulation using a drone kit with a software-in-the-loop simulator. The ONLINE and OFFLINE path planning algorithm is applied to discover a path to the destination in a changeable situation, and it is simulated on a real-life map, which includes a restricted area.

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References

  1. Tariq R, Rahim M, Aslam N, Bawany N, Faseeha U (2018) DronAID: a smart human detection drone for rescue. In: 2018 15th international conference on smart cities: improving quality of life using ICT & IoT (HONET-ICT), Islamabad, pp 33–37. https://doi.org/10.1109/HONET.2018.8551326

  2. Mozaffari M, Saad W, Bennis M, Nam Y, Debbah M (2019) A tutorial on UAVs for wireless networks: applications, challenges, and open problems. IEEE Commun Surv Tutor 21(3):2334–2360. https://doi.org/10.1109/COMST.2019.2902862

    Article  Google Scholar 

  3. Erdelj M, Natalizio E, Chowdhury KR, Akyildiz IF (2017) Help from the sky: leveraging UAVs for disaster management. IEEE Pervasive Comput 16(1):24–32

    Article  Google Scholar 

  4. Bassi E (2019) European drones regulation: today’s legal challenges. In: 2019 international conference on unmanned aircraft systems (ICUAS), Atlanta, GA, USA, pp 443–450. https://doi.org/10.1109/ICUAS.2019.8798173

  5. Unmanned Aircraft Systems (2016) https://www.faa.gov/uas/. Accessed 7 Mar 2016

  6. Federal Aviation Adminstration (FAA) (2016) DC is a no drone zone. http://www.faa.gov/uas/no_drone_zone/dc/. Accessed 4 Jun 2016

  7. Namuduri K, Wan Y, Gomathisankaran M, Pendse R (2012) Airborne network: a cyber-physical system perspective. In: Proceedings 1st ACM MobiHoc workshop airborne network communications, Hilton Head, SC, USA, pp 55–60

  8. Motlagh NH, Taleb T, Arouk O (2016) Low-altitude unmanned aerial vehicles-based internet of things services: comprehensive survey and future perspectives. IEEE Internet Things J 3(6):899–922

    Article  Google Scholar 

  9. Cochez M, Periaux J, Terziyan V, Kamlyk K, Tuovinen T (2014) Evolutionary cloud for cooperative UAV coordination. University Jyväskylä, Jyväskylä

    Google Scholar 

  10. Janarthanan A, Ho HW, Gopal L, Shanmugam V, Wong WK (2019) An unmanned aerial vehicle framework design for autonomous flight path. In: 2019 7th international conference on smart computing and communications (ICSCC), Sarawak, Malaysia, Malaysia, pp 1–5. https://doi.org/10.1109/ICSCC.2019.8843618

  11. Yang L, Qi J, Xiao J, Yong X (2014) A literature review of UAV 3D path planning. In: Proceeding of the 11th world congress on intelligent control and automation, Shenyang, pp 2376–2381

  12. Yanmaz E, Kuschnig R, Quaritsch MR, Bettstetter C, Rinner B (2011) On path planning strategies for networked unmanned aerial vehicles. In: 2011 IEEE conference on computer communications workshops (INFOCOM WKSHPS), Shanghai, pp 212–216

  13. Mcfadyen A, Mejias L (2016) A survey of autonomous vision-based see and avoid for unmanned aircraft systems. Prog Aerosp Sci 80:1–17

    Article  Google Scholar 

  14. Tulum K, Durak U, Yder SK (2009) Situation aware UAV mission route planning. In: 2009 IEEE aerospace conference, Big Sky, MT, pp 1–12

  15. Hernández-Hernández L, Tsourdos A, Shin HS, Waldock A (2014) Multi-objective UAV routing. In: 2014 international conference on unmanned aircraft systems (ICUAS), Orlando, FL, pp 534–542

  16. Mokrane A, Braham AC, Cherki B (2020) UAV path planning based on dynamic programming algorithm on photogrammetric DEMs. In: 2020 international conference on electrical engineering (ICEE), Istanbul, Turkey, pp 1–5. https://doi.org/10.1109/ICEE49691.2020.9249903

  17. Yin C, Xiao Z, Cao X, Xi X, Yang P, Wu D (2018) Offline and online search: UAV multiobjective path planning under dynamic urban environment. IEEE Internet Things J 5(2):546–558. https://doi.org/10.1109/JIOT.2017.2717078

    Article  Google Scholar 

  18. Jacob S, Menon VG, Parvathi R, Shynu PG, Shemim KSF, Mahapatra B, Mukherjee M (2020) Intelligent vehicle collision avoidance system using 5G-enabled drone swarms. In: DroneCom '20: proceedings of the 2nd ACM MobiCom workshop on drone assisted wireless communications for 5G and beyond, pp 91–96. https://doi.org/10.1145/3414045.3415938

  19. Li C, Xie X, Luo F (2019) Obstacle detection and path planning monocular vision for unmanned aerial vehicles. In: Chinese automation congress (CAC), pp 3305–3309. https://doi.org/10.1109/CAC48633.2019.8996752

  20. Daramouskas I, Perikos I, Hatzilygeroudis I, Lappas VJ, Kostopoulos V (2020) A methodology for drones to learn how to navigate and avoid obstacles using decision trees. In: 11th international conference on information, intelligence, systems and applications (IISA), pp 1–4. https://doi.org/10.1109/IISA50023.2020.9284337

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Funding

Funding was provided by the National Research Foundation of Korea (Grant Nos. NRF-2018R1D1A1B07040573, NRF-2019R1I1A1A01063619).

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  1. Ajou University, Suwon, Republic of Korea

    Kwanghoon Choi & Jai-Hoon Kim

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  1. Kwanghoon Choi
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  2. Jai-Hoon Kim
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Correspondence to Jai-Hoon Kim.

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Choi, K., Kim, JH. UAV path planning method for avoiding restricted areas. Intel Serv Robotics 14, 679–690 (2021). https://doi.org/10.1007/s11370-021-00386-3

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  • DOI: https://doi.org/10.1007/s11370-021-00386-3

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