Skip to main content

Advertisement

SpringerLink
Log in

Two-phase multi-expert knowledge approach by using fuzzy clustering and rule-based system for technology evaluation of unmanned aerial vehicles

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Unmanned aerial vehicles (UAVs) are utilized in many different areas for different aims such as the benefit of humanity, safety control, traffic control, crop monitoring, scientific research, and commercial applications. Moreover, the UAVs are also successfully utilized for military operations, such as surveillance of an area and counter-terrorism actions. Evaluating them through the technological perspective is quite significant and should be considered from multiple perspectives. In this context, it will be more beneficial to construct a methodology for an efficient evaluation process. The fuzzy set theory (FST) can also be integrated into this methodology to improve its sensitiveness and flexibility. In this paper, a novel methodology integrating fuzzy \(c\)-means (FCM) clustering and fuzzy inference system (FIS) has been suggested for the technical evaluation of UAVs. While the FCM clustering algorithm has been utilized to determine the clusters, rules have been created for the FIS through expert assessments, and alternative UAV technologies have been prioritized. For the evaluation procedure, the hierarchical structure of the technology evaluation features has been determined by fusing expert knowledge, literature review, and related ISO standards. Through the FCM clustering algorithm, alternative vehicles have been clustered based on the sub-features of each main feature. Then, FIS has been conducted by using experts’ knowledge from the fields of military technologies in UAVs and armed UAVs to obtain the technology indices of the eight UAVs locally produced and used in Turkey. The results demonstrate that the proposed methodology can be successfully applied by the managers or research and development (R&D) engineers for evaluation of the UAV technologies to consider cardinal and linguistic data. Additionally, a comparative analysis based on self-organizing map (SOM) and fuzzy \(k\)-means algorithms has also been applied for the proposed method, and their performances have been compared.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  1. Saleem Y, Rehmani MH, Zeadally S (2015) Integration of cognitive radio technology with unmanned aerial vehicles: issues, opportunities, and future research challenges. J Netw Comput Appl 50:15–31. https://doi.org/10.1016/j.jnca.2014.12.002

    Article  Google Scholar 

  2. Aminifar F, Rahmatian F (2020) Unmanned aerial vehicles in modern power systems: technologies, use cases, outlooks, and challenges. IEEE Electrif Mag 8(4):107–116. https://doi.org/10.1109/MELE.2020.3026505

    Article  Google Scholar 

  3. Škrinjar JP, Škorput P, Furdić M (2019) Application of unmanned aerial vehicles. In: Logistic processesin lecture notes in networks and systems, vol. 42. Springer, 2019, pp 359–366

  4. Gao Y, Li D (2019) Consensus evaluation method of multi-ground-target threat for unmanned aerial vehicle swarm based on heterogeneous group decision making. Comput Electr Eng 74:223–232. https://doi.org/10.1016/j.compeleceng.2019.01.019

    Article  Google Scholar 

  5. Yu X, Li C, Yen GG (2020) A knee-guided differential evolution algorithm for unmanned aerial vehicle path planning in disaster management. Appl Soft Comput. https://doi.org/10.1016/j.asoc.2020.106857

    Article  Google Scholar 

  6. Hang Wang B, Bo Wang D, Anwar Ali Z, Ting Ting B, Wang H (2019) An overview of various kinds of wind effects on unmanned aerial vehicle. Meas Control 52(8):731–739. https://doi.org/10.1177/0020294019847688

    Article  Google Scholar 

  7. Yan F, Zhu X, Zhou Z, Tang Y (2019) Heterogeneous multi-unmanned aerial vehicle task planning: simultaneous attacks on targets using the Pythagorean hodograph curve. Proc Inst Mech Eng Part G J Aerosp Eng 233(13):4735–4749. https://doi.org/10.1177/0954410019829368

    Article  Google Scholar 

  8. Shakhatreh H et al (2019) Unmanned aerial vehicles (UAVs): a survey on civil applications and key research challenges. IEEE Access 7:48572–48634. https://doi.org/10.1109/ACCESS.2019.2909530

    Article  Google Scholar 

  9. Kurnaz S, Cetin O, Kaynak O (2010) Adaptive neuro-fuzzy inference system based autonomous flight control of unmanned air vehicles. Expert Syst Appl 37(2):1229–1234. https://doi.org/10.1016/j.eswa.2009.06.009

    Article  Google Scholar 

  10. Yang TW, et al (2012) Overhead power line detection from UAV video images. In: IEEE conference publication, in 2012 19th international conference on mechatronics and machine vision in practice (M2VIP), 2012

  11. Fallahi K, Leung H, Chandana S (2009) An integrated ACO-AHP approach for resource management optimization. In: Conference proceedings—IEEE international conference on systems, man and cybernetics, pp 4335–4340. https://doi.org/10.1109/ICSMC.2009.5346794

  12. Sun G, Ma H, Zhao D, Zhang F, Jia L, Sun J (2015) Oil spill image segmentation based on fuzzy C-means algorithm, 2015, pp 406–409. https://doi.org/10.2991/csic-15.2015.98

  13. Zhao S, Wang X, Zhang D, Shen L (2017) model-free fuzzy adaptive control of the heading angle of fixed-wing unmanned aerial vehicles. J Aerosp Eng 30(4):04017019. https://doi.org/10.1061/(asce)as.1943-5525.0000730

    Article  Google Scholar 

  14. Chen L, Mantegh I, He T, Xie W (2020) Fuzzy kinodynamic RRT: a dynamic path planning and obstacle avoidance method. In: 2020 International conference on unmanned aircraft systems, ICUAS 2020, pp 188–195. https://doi.org/10.1109/ICUAS48674.2020.9213964

  15. Sathyan A, Ernest ND, Cohen K (2016) An efficient genetic fuzzy approach to UAV swarm routing. Unmanned Syst 04(02):117–127. https://doi.org/10.1142/s2301385016500011

    Article  Google Scholar 

  16. Woźniak M, Połap D (2020) Intelligent home systems for ubiquitous user support by using neural networks and rule-based approach. IEEE Trans Ind Inf 16(4):2651–2658. https://doi.org/10.1109/TII.2019.2951089

    Article  Google Scholar 

  17. Goswami M, Arya R, Prateek (2021) UAV communication in FANETs with metaheuristic techniques. In: Advances in intelligent systems and computing, 2021, vol 1162, pp 1–11. https://doi.org/10.1007/978-981-15-4851-2_1

  18. Petkovics I, Simon J, Petkovics A, Covic Z (2017) Selection of unmanned aerial vehicle for precision agriculture with multi-criteria decision making algorithm. In: SISY 2017—IEEE 15th international symposium on intelligent systems and informatics, proceedings, pp 151–155. https://doi.org/10.1109/SISY.2017.8080543

  19. Korytkowski M, Scherer R, Szajerman D, Polap D, Wozniak M (2020) Efficient visual classification by fuzzy rules. In: IEEE international conference on fuzzy systems, 2020, vol. 2020-July. https://doi.org/10.1109/FUZZ48607.2020.9177777

  20. Ansari RI, Ashraf N, Politis C (2020) An energy-aware distributed open market model for UAV-assisted communications. In: IEEE vehicular technology conference, 2020, vol. 2020-May. https://doi.org/10.1109/VTC2020-Spring48590.2020.9128475

  21. Ferdaus MM, Anavatti SG, Garratt MA, Pratama M (2017) Fuzzy clustering based modelling and adaptive controlling of a flapping wing micro air vehicle. In: 2017 IEEE symposium series on computational intelligence, SSCI 2017—proceedings, 2018, vol. 2018-January, pp 1–6, doi: https://doi.org/10.1109/SSCI.2017.8280969

  22. Dorzhigulov A, Bissengaliuly B, Spencer BF, Kim J, James AP (2018) ANFIS based quadrotor drone altitude control implementation on Raspberry Pi platform. Analog Integr Circuits Signal Process 95(3):435–445. https://doi.org/10.1007/s10470-018-1159-8

    Article  Google Scholar 

  23. Ercan C, Gencer C (2018) A decision support system for dynamic heterogeneous unmanned aerial system fleets, Sep. 2018

  24. Raj A, Sah B (2019) Analyzing critical success factors for implementation of drones in the logistics sector using grey-DEMATEL based approach. Comput Ind Eng. https://doi.org/10.1016/j.cie.2019.106118

    Article  Google Scholar 

  25. Yang Z, Pan C, Wang K, Shikh-Bahaei M (2019) Energy efficient resource allocation in UAV-enabled mobile edge computing networks. IEEE Trans Wirel Commun 18(9):4576–4589. https://doi.org/10.1109/TWC.2019.2927313

    Article  Google Scholar 

  26. Pratama M, Anavatti SG, Garratt M, Lughofer E (2013) Online identification of complex multi-input-multi-output system based on generic evolving neuro-fuzzy inference system. In: Proceedings of the 2013 IEEE conference on evolving and adaptive intelligent systems, EAIS 2013. 2013 IEEE symposium series on computational intelligence, SSCI 2013, pp 106–113. https://doi.org/10.1109/EAIS.2013.6604112

  27. Messous MA, Sedjelmaci H, Senouci SM (2017) Implementing an emerging mobility model for a fleet of UAVs based on a fuzzy logic inference system. Pervasive Mob Comput 42:393–410. https://doi.org/10.1016/j.pmcj.2017.06.007

    Article  Google Scholar 

  28. Dovgal VA (2020) Decision-making for placing unmanned aerial vehicles to implementation of analyzing cloud computing cooperation applied to information processing. In: Proceedings—2020 international conference on industrial engineering, applications and manufacturing, ICIEAM 2020, 2020. https://doi.org/10.1109/ICIEAM48468.2020.9111975

  29. Zhong Y, Yao P, Sun Y (2016) Decision-making allocation method in manned/unmanned combat aerial vehicle cooperative engagement, Xitong Gongcheng Lilun yu Shijian/System Eng. Theory Pract 36(11):2984–2992. https://doi.org/10.12011/1000-6788(2016)11-2984-09

    Article  Google Scholar 

  30. Wozniak M, Zielonka A, Sikora A, Piran MJ, Alamri A (2020) 6G-enabled IoT home environment control using fuzzy rules. IEEE Internet Things J. https://doi.org/10.1109/JIOT.2020.3044940

    Article  Google Scholar 

  31. Nawaz H, Ali HM, Massan SUR (2019) Applications of unmanned aerial vehicles: a review. In: 3C Tecnología. Glosas de innovación aplicadas a la pyme. Special Issue, 2019, 85–105. https://doi.org/10.17993/3ctecno.2019.specialissue3.85-105

  32. Ian H, Witten E, Frank, Hall MA (2011) Data mining: practical machine learning tools and techniques. Morgan Kaufmann

  33. Spath H (1980) Cluster analysis algorithms (computers and their applications). Halsted Pr, 1980

  34. Dayan P, Dayan P, Sahani M, Deback G (1999) Unsupervised learning. MIT Encycl. Cogn. Sci

  35. Anderberg MR (2014) Cluster analysis for applications: probability and mathematical statistics: a series of monographs and textbooks. Academic Press, New York

    Google Scholar 

  36. Xu R, Wunsch D (2005) Survey of clustering algorithms. IEEE Trans Neural Netw 16(3):645–678. https://doi.org/10.1109/TNN.2005.845141

    Article  Google Scholar 

  37. Cherkassky V, Mulier FM (2007) Learning from data: concepts, theory, and methods, 2nd edn. Wiley, Hoboken

    Book  Google Scholar 

  38. Gustafson DE, Kessel WC (1978) Fuzzy clustering with a fuzzy covariance matrix. In: Proceedings of the IEEE conference on decision and control, pp 761–766. https://doi.org/10.1109/cdc.1978.268028

  39. Gath I, Geva AB (1989) Unsupervised optimal fuzzy clustering. IEEE Trans Pattern Anal Mach Intell 11(7):773–780. https://doi.org/10.1109/34.192473

    Article  MATH  Google Scholar 

  40. Zadeh LA (1965) Fuzzy sets. Inf Control 8(3):338–353. https://doi.org/10.1016/S0019-9958(65)90241-X

    Article  MATH  Google Scholar 

  41. Zimmermann H-J (2001) Fuzzy set theory - and its applications. Springer, Amsterdam

    Book  Google Scholar 

  42. Karaşan A, Kahraman C (2018) Interval-valued neutrosophic extension of EDAS method. Adv Intell Syst Comput 642:343–357. https://doi.org/10.1007/978-3-319-66824-6_31

    Article  Google Scholar 

  43. Bezdek JC (1981) Pattern recognition with fuzzy objective function algorithms. Springer, New York

    Book  Google Scholar 

  44. Jouffe L (1998) Fuzzy inference system learning by reinforcement methods. IEEE Trans Syst Man Cybern Part C Appl Rev 28(3):338–355. https://doi.org/10.1109/5326.704563

    Article  Google Scholar 

  45. Zadeh LA (1996) Fuzzy logic = computing with words. IEEE Trans Fuzzy Syst 4(2):103–111. https://doi.org/10.1109/91.493904

    Article  Google Scholar 

  46. Mamdani EH, Assilian S (1975) An experiment in linguistic synthesis with a fuzzy logic controller. Int J Man Mach Stud 7(1):1–13. https://doi.org/10.1016/S0020-7373(75)80002-2

    Article  MATH  Google Scholar 

  47. Takagi T, Sugeno M (1985) Fuzzy identification of systems and its applications to modeling and control. IEEE Trans Syst Man Cybern 15(1):116–132. https://doi.org/10.1109/TSMC.1985.6313399

    Article  MATH  Google Scholar 

  48. Guillaume S (2001) Designing fuzzy inference systems from data: an interpretability-oriented review. IEEE Trans Fuzzy Syst 9(3):426–443. https://doi.org/10.1109/91.928739

    Article  Google Scholar 

  49. Zeng J, An M, Smith NJ (2007) Application of a fuzzy based decision-making methodology to construction project risk assessment. Int J Project Manag 25(6):589–600

    Article  Google Scholar 

  50. DeBusk WM (2010) Unmanned aerial vehicle systems for disaster relief: Tornado alley. In: AIAA Infotech at aerospace. https://doi.org/10.2514/6.2010-3506

  51. “ISO - ISO 21384-3:2019 - Unmanned aircraft systems—Part 3: Operational procedures.” [Online]. Available: https://www.iso.org/standard/70853.html. [Accessed: 17-Feb-2021]

  52. Ferrão IG, et al (2020) STUART: ReSilient archiTecture to dynamically manage Unmanned aeriAl vehicle networks under attack. In: Proceedings—IEEE symposium on computers and communications, vol. 2020. https://doi.org/10.1109/ISCC50000.2020.9219689

  53. Fraga-Lamas P, Ramos L, Mondéjar-Guerra V, Fernández-Caramés TM (2019) A review on IoT deep learning UAV systems for autonomous obstacle detection and collision avoidance. Remote Sens 11(18):2144. https://doi.org/10.3390/rs11182144

    Article  Google Scholar 

  54. Kim A, Kim M, Puchaty E, Sevcovic M, Delaurentis D (2010) A system-of-systems framework for the improved capability of insurgent tracking missions involving unmanned aerial vehicles. In: 2010 5th international conference on system of systems engineering, SoSE 2010. https://doi.org/10.1109/SYSOSE.2010.5544076

  55. ANSI Unmanned Aircraft Systems Standardization Collaborative–UASSC. https://www.ansi.org/standards-coordination/collaboratives-activities/unmanned-aircraft-systems-collaborative. Accessed: 17-Feb-2021

  56. Liu Y, Dai HN, Wang Q, Shukla MK, Imran M (2020) Unmanned aerial vehicle for internet of everything: opportunities and challenges. Comput Commun 155:66–83. https://doi.org/10.1016/j.comcom.2020.03.017

    Article  Google Scholar 

  57. Hamurcu M, Eren T (2020) Selection of unmanned aerial vehicles by using multicriteria decision-making for defence. J Math. https://doi.org/10.1155/2020/4308756

    Article  MathSciNet  Google Scholar 

  58. Hung KC, Yin M, Lin KP (2009) Enhancement of fuzzy weighted average and application to military UAV selected under group decision making. In: 6th international conference on fuzzy systems and knowledge discovery, FSKD 2009, vol. 7, pp 191–195. https://doi.org/10.1109/FSKD.2009.84

  59. Lin KP, Hung KC (2011) An efficient fuzzy weighted average algorithm for the military UAV selecting under group decision-making. Knowledge-Based Syst 24(6):877–889. https://doi.org/10.1016/j.knosys.2011.04.002

    Article  Google Scholar 

  60. Zhang H, Xin B, Hua Dou L, Chen J, Hirota K (2020) A review of cooperative path planning of an unmanned aerial vehicle group. Front Inf Technol Electron Eng 21(12):1671–1694. https://doi.org/10.1631/FITEE.2000228

    Article  Google Scholar 

  61. Saeed AS, Younes AB, Cai C, Cai G (2018) A survey of hybrid unmanned aerial vehicles. Progress in aerospace sciences, vol. 98. Elsevier Ltd, pp 91–105. https://doi.org/10.1016/j.paerosci.2018.03.007

  62. ICAO Cir 328 (2011) Unmanned aircraft systems (UAS), Montréal

  63. Karasakal O, Karasakal E, Maraş G (2020) Multiobjective aerial surveillance over disjoint rectangles. Comput Ind Eng 148:106732. https://doi.org/10.1016/j.cie.2020.106732

    Article  Google Scholar 

  64. ISO - ISO 23665:2021 - Unmanned aircraft systems—training for personnel involved in UAS operations.” [Online]. https://www.iso.org/standard/76592.html. Accessed 17-Feb-2021

Download references

Funding

This study has not been funded.

Author information

Authors and Affiliations

  1. Department of Industrial Engineering, Kocaeli University, 41380, Izmit, Kocaeli, Turkey

    Murat Çolak

  2. Department of Industrial Engineering, Yildiz Technical University, 34349, Besiktas, Istanbul, Turkey

    İhsan Kaya & Ali Karaşan

  3. Department of Industrial Engineering, Düzce University, 81620, Konuralp, Duzce, Turkey

    Melike Erdoğan

Authors
  1. Murat Çolak
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. İhsan Kaya
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ali Karaşan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Melike Erdoğan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to İhsan Kaya.

Ethics declarations

Conflict of interest

All authors state that there is no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix

Appendix

See Table 11.

Table 11 Evaluations of the consensus with respect to main features for the determination of the clusters for fuzzy c-means clustering
Full size table

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Çolak, M., Kaya, İ., Karaşan, A. et al. Two-phase multi-expert knowledge approach by using fuzzy clustering and rule-based system for technology evaluation of unmanned aerial vehicles. Neural Comput & Applic 34, 5479–5495 (2022). https://doi.org/10.1007/s00521-021-06694-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-021-06694-0

Keywords

Search

Navigation